phd research projects

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This project will examine the unique 3.5 million year old megafauna fossils from Chinchilla Rifle Range, Queensland. The project will focus on the taphonomy of the site, and the sequence of fossils collected in systematically excavated sites. Several unusual fossils are awaiting description and taxonomic identification, and palaeoenvironmental proxies revealing ancient Australian habitats can be further interogated.

Palaeontology

EarthScience

This project aims to recover all the genetic information from four ancient humans. Two of these iconic specimens come from Australia and two from Malaysia. We will sequence the entire DNA (genomes) and proteins (proteome) of Mungo Man (Willandra), as well as the Yidinji King (Cairns), the Deep Skull (Borneo) and the Bewah specimen (Malaysian Peninsula). This will provide a better understanding of the settlement of Australia and new knowledge about the ancient people of Australasia and their relationship to other human populations worldwide. The research will use cutting-edge methods of DNA and protein sequencing of ancient human material and will provide critical reference genomes / proteomes that will anchor future research.

Evolutionary Biology

BiologicalScience

Coral reefs are complex ecosystems but are under threat from anthropogenic activities. When reefs degrade, corals are normally replaced by macroalgae, therefore understanding macroalgal ecology is critical for the conservation of the Great Barrier Reef (GBR). This project aims at providing fundamental knowledge of the ecological processes involved in macroalgal blooms in the GBR.

Oceanography

EarthScience

Aquaculture is one of the fastest growing food sectors in the world, with great potential for expansion. Climate change poses a significant threat to aquaculture production - from potential losses in infrastructure to sub-optimal growth and production rates, but climate change is rarely included in aquaculture development plans. In this project you will work with an interdisciplinary team to assess and incorporate climate risk into aquaculture planning to future proof aquaculture production under a changing climate.

Food systems, Climate Change

EnvironmentalScience

Aquaculture is a fast growing industry in Australia - which is known for production safe and relatively sustainable seafood products. Australia has great potential for aquaculture expansion but currently has limited knowledge of how aquaculture impacts the environment now and in the future. This project will work to quantify major environmental impacts from Australian aquaculture (nutrient pollution, GHG emissions, etc.) and potential impacts on habitat, species and ecosystem services. The work is essential for sustainable Australian aquaculture and supports many of Australia's Blue Economy initiatives.

Conservation, Food systems

EnvironmentalScience

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. Marine restoration efforts are ramping up due to global initiatives and local success stories. While restoration is needed, it is also crucial to understand and elimate threats that degraded land and seascapes to begin with. This project will assess the potential risk of land-based run-off of marine restoration and prioritise areas to focus future efforts.

Conservation, land-sea planning

Waste Water Management, EnvironmentalScience

Marine protected areas are the main conservation tool used to address the biodiversity crisis in our oceans. They are also a major focus of international conservation agreements such as the recently adopted Kunming-Montreal Global Biodiversity Framework. This project will use novel methods to quantify human impacts in marine protected areas through time and develop strategies and recommendations to reduce these impacts and improve the effectiveness of marine protected areas.

Conservation planning

EnvironmentalScience

Land-based run-off is one of the greatest threats to marine ecosystems, following climate change. However, it is largely ignored in international agreements. Those that do aim to address the issue largley focus on plastics and nutrients but often ignore sediments. This project will explore how international conservation agreements can be better leveraged to reduce all aspects land-based run-off.

Conservation, land-sea planning

Waste Water Management, EnvironmentalScience

Using inherent properties of natural ecosystems to manageme environmental problems has long been used by traditional custodians of the land, but has more recently been incorporated into global conservation and environmental strategies. Identifying priority areas, optimal management interventions, and their potential benefits remain a key area of research for meeting environmental goals. This project will develop frameworks for prioritising nature-based solutions under increasing anthropogenic threats, including climate change.

Conservation planning

EnvironmentalScience

Offshore wind energy is a promising way to meeting growing energy demand along with carbon emission reduction targets. However, given that the industry is relatively new, there are still many questions to answer regarding it's impact on nature and how to minimise these impacts with smart planning. This project will aim to address these questions to better understand the impacts and environmental thresholds to inform offshore wind development within Australia and globally

Conservation planning

EnvironmentalScience

Nearly 1/3 of coral reefs are threatened by poor qater quality and there are an estimated 800,000 human deaths each year due to sanitaiton-related water pollution. Improved sanitation has the potential to achieve benefits for both nature and people - but is often poorly understood (particularly in communities with little access to resources). This project will asess opportunities for reducing nutrient pollution to achieve both ecosystem and human health objectives - with the potential to incorporate risk and uncertainty from climate impacts.

Conservation, land-sea planning

Waste Water Management, EnvironmentalScience

The project will investigate the fate and effects of firefighting chemicals and bushfire leachates in Eastern Australian waterways to assess the risk they pose to aquatic organisms and ecosystems on the short term and long term. Firefighting chemicals are deployed by emergency services for the protection of life and property, however there is a gap in the knowledge associated with their short- and long-term effects to water quality and aquatic ecosystems. This will be a largely lab-based experimental project and will aim to better understand if and at what scale these chemicals impact aquatic ecosystems and the timescales associated with these potential impacts. Other lines of evidence will also be explored such as the identification of ‘signatures’ associated with firefighting chemicals to better understand the contribution they have to water quality impacts in a large severely burnt catchment. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to work across each location. Focus areas are bushfire-related aquatic ecotoxicology, environmental pollution, and environmental chemistry.

Ecotoxicology; Environmental pollution; Environmental chemistry; Bushfire

EnvironmentalScience

As restoration projects gain traction during this Decade for Ecosystem Restoration, we need to develop techniques that secure the success of these projects and achieve expected outcomes. The project works with many stakeholders including Traditional Owners, farmers, State Government and local council to determine whether ecosystem services, including nutrient retention, carbon sequestration and biodiversity, develop within current wetland restoration projects.

Ecosystem function

EarthScience

Wetlands can accumulate large amounts of carbon, but when disturbed, this carbon can be released into the atmosphere as CO2 and CH4, contributing to global warming. This project aims to determine how disturbances, including hydrological modifications, feral animals and deforestation, affect the carbon cycle of wetlands (mangroves, marshes and supratidal forests) and how can these be reversed.

Oceanography

EarthScience

Macroalgae or seaweeds are a fundamental component of the Great Barrier Reef, but their diversity is poorly known. This project aims at discovering and documenting the diversity of marine benthic algae using molecular methods for a better understanding of their natural history and roles in coral reefs.

Oceanography

EarthScience

Help us save the sea turtles! Chemical contaminants are accumulating in marine wildlife worldwide. However, due to their large size and often protected status, there are ethical and logistical constraints in conducting traditional whole animal toxicity tests on these animals. Recently, cell-based bioassays have been proposed as an ethical alternative to assessing the effects of contaminants in marine megafauna. This project aims to establish marine wildlife cell cultures and develop species-specific cell-based toxicity bioassays to assess the effects of chemical pollutants in marine wildlife. This project will involve both field and lab components, and include collaborations with state and federal government agencies, non-profit conservation organisations and the private sector

Oceanography

Environmental Science

Advancements in analytical capabilities make it possible to simultaneously measure a comprehensive suite of physiologically important biomolecules in living organisms. These molecules can provide a ‘snapshot’ of the health and general well-being of an organism. This research project aims to establish robust methodologies to make molecular monitoring a reality. The PhD project will apply untargeted metabolomics and lipidomics analysis to evaluate and compare the status of aquatic species from pristine and human-impacted locations, with the goal of establishing biomolecular signatures as indicators of environmental health.

Applied environmental metabolomics

EnvironmentalScience

As part of the ARC Discovery project, work would be undertaken to examine the triggers for formation of cyanobacteria blooms using a combination of individual based modelling of at a cell or colony level in cyanobacteria, and supporting this work with mesocos trials across lakes in Australia, China and Germany.

Climate change, reservoir, drinking water, water resources 

EnvironmentalScience

Blue-green algal blooms dominate many Australian lakes and reservoirs. Toxic species create major problems for drinking water and recreation. We work collaboratively with environmental and water managers to determine the factors controlling these blooms with both field and lab work.

Oceanography

EarthScience

Nutrient offsetting provides a market based mechanism for restoring catchments to improve the water quality in rivers and the coasts. Point source polluters pay to restore non-point source pollution in catchments. However, there are significant gaps in knowledge in comparing point and non point sources of nutrients in terms of how they affect the environment. This project will work collaboratively with industry and government to examine these nutrient sources and link them to nutrient responses in the environment.

Oceanography

EarthScience

Our research has found that leaves from trees leach organic matter that can negatively effect algae. However, at the catchment level it is unclear how much impact the organic matter from trees is having on algal blooms. This research would involve working with the water industry to tackle this question.

Ecosystem function

EarthScience

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

Oceanography

EnvironmentalScience

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Oceanography

EnvironmentalScience

Changes in fire regime and global warming are significant and interactive symptoms of climate change. In this study we would like to investigate the long-term, interactive impacts of fire and warming on soil C dynamics and soil-to-atmosphere C fluxes in different ecosystems

Biogeochemistry

EnvironmentalScience

Elevated levels of terrigenous sediments in river systems has long been regarded as one of the most deteriorating factors on water quality in rivers and coastal area. However, the land use sources of sediments in rivers systems are uncertain. In this study we will develope novel biogeochemical fingerprinting models for tracing the terrestrial source of sediment and nutrient in river systems.

Biogeochemistry

EnvironmentalScience

We are currently looking for a PhD candidate to work on Soil Ameliorants. The primary purpose of this role is to develop a series of novel Soil Ameliorants from locally available materials or wastes. The ideal candidate needs to have a relevant background in chemistry. Success in this role requires collaboration with research partners, industry and farmers. This PhD project will be based on Nathan Campus, Griffith University.

Material and environmental science

ChemicalSciences

this project aims to improve our understanding of how ecosystem processes affect soil carbon quality and quantity, and how this in turn influences soil resilience to environmental stresses (e.g. drought, compaction, chemical residues of fungicides, and carbon decline) and to develop sensitive and affordable assessment protocols for improvement of soil carbon stocks and functional resilience to environmental stresses.

Soil Science

EnvironmentalScience

Biochar is a solid by-product of thermochemical conversion of biomass (in the absence or reduction of oxygen) to bio-oil and syngas, which is dominantly composed of aromatic compounds resistant to biological degradation. Biochar would enhance soil aeration, increase soil pH, favour nitrogen immobilization, interact with available organic C and N in soil, act as an electron shuttle for soil microorganisms and modify soil enzyme activities as well as microbial abundance and community composition. This project aims to investigate how modification of pyrolysis process (i.e., pyrolysis temperature; heating rate; residence time) and co-pyrolysis of biosolid with organic wastes (i.e., feedstock type; blending ratio) would reduce the environmental risks associated with biosolid (i.e., heavy metals; microplastics; PAHs; PFAS), while improve its quality (i.e., C content, specific surface area; porous structure; water holding capacity) for application in agricultural systems.

Soil Science

EnvironmentalScience

Bioretention systems are excavated basins or trenches that are filled with porous filter media and planted with vegetation to remove pollutants from stormwater runoff.The main aim of this project is to examine the impacts of locally available recycled organic amendments on improvement of plants performance and reduction of nutrient leaching from bioretention filter media. The main objective is to design a cost-effective and functional bioretention filter media with optimum nutrient retention capacity and carbon storage for supporting sustainable plant performance in bioretention systems.

Soil Science

EnvironmentalScience

Microplastics (MPs) are a major emerging contaminant in agroecosystems, due to their significant resistance to degradation in terrestrial environments. This project asseses the characteristics and fate of MPs in contaminated soils and their risks to soil biota.

Soil Science

EnvironmentalScience

Seawater is the most abundant aqueous resource on earth that is readily accessible at very low costs, but yet to be directly utilised for production of hydrogen fuel and commodity chemicals. This project aims to develop cheap and plentiful carbon-based high performance chlorine evolution electrocatalysts for seawater electrolysis powered by renewable electricity to realise the production of hydrogen, chlorine and sodium hydroxide directly from seawater. The electrolyser can also be used to treat desalination brine while produce hydrogen and chemicals. The success of the project will set a firm technological foundation for seawater utilisation, which will add to Australian capability to meet future energy and environment challenges.

Functional materials, Electrochemistry

ChemicalSciences

The current industrial-scale hydrogen productions are reliant on high temperature steam reforming fossil fuels, consuming large quantity of energy and fossil resources, and emitting huge amounts of CO2. This project aims to develop cheap and plentiful transition metal-based high performance water splitting electrocatalysts, enabling economically viable large-scale water electrolytic hydrogen production driven by renewable electricity. A theory-guided catalyst approach will be used to guide the efficient design and development of high performance electrocatalysts. The success of the project will lead to a suit of high performance water splitting electrocatalysts, leaping forward water electrolytic hydrogen production technology.

Functional materials, Electrochemistry

ChemicalSciences

This project aims to develop nano-catalysts with high catalytic activity and rapid gas detachment properties for efficient fuel gas production. Heterogeneous electrocatalytic gas evolution reactions are important for clean energy generation and storage technologies, but high overpotentials caused by slow gaseous products’ detachment from catalyst surface severely hinder their efficiencies. Expected outcomes include insights into gas bubble formation and evolution during electrocatalysis, effective catalyst structures to mitigate negative effects of gas bubble formation, and improved catalytic efficiency of gas evolution reactions and develop high performance electrocatalysts for fuel gas production.

Functional materials, Electrochemistry

ChemicalSciences

Nanoparticles have a great potential to be used in water treatment due to its high surface area. This can be utilised efficiently for removing toxic metal ions, microbes and organic matter from water. However, due to their sizes, nanoparticles often form aggregates/agglomerates lowering their activities. To prevent these, further processing including surface passivation is applied. The use of activated carbon into the nanoparticle systems is another strategy as it is simple and economical. Activated carbon was recorded to be used for a multitude of applications, including water filtration/treatment, gas phase adsorption and decolourising agents in the food industry. Research into improving both structure and applications has grown exponentially in recent decades as environmental sustainability has become a key focus, especially the areas involved in environmental remediation. Combination of the nanoparticle and activated carbon provides an excellent platform for the environmental applications such as enhanced capacities and rates. We have developed a system consisting of iron nanoparticles impregnated onto activated carbon. The synthesised materials demonstrated substantially higher arsenic (III) adsorption efficiency/capacity in comparison with nanoparticles and activated carbon alone. The aim of this project is to extend these results producing comprehensive systems for the removal of well-known toxins such as PFAS.

Material Science, Analytical Chemistry, Nanomaterials, Environmental Remediation

ChemicalSciences

Antimicrobial therapies have been a magic bullet against infectious diseases since their introduction. However, due to the excessive use of antibiotics via irrelevant and unregulated access, the efficacy of the antibiotic has declined rapidly in parallel with increases in antibiotic resistant bacterial strains. Resistance to this antibiotic has risen rapidly and its clinical usefulness has declined to a point that it is now rarely considered a frontline treatment option. With the emergence of resistant Gram-negative ‘superbugs’, infections caused by multidrug-resistant Gram-negative bacteria have been named as one of the most urgent global health issues due to the lack of effective drugs. Numerous research for new antibiotics focus on developing improved versions of existing molecules, Amongst these new designed and engineered drugs, nanosized particles have gained much recent attention due to their physical size, biocompatibility and functionalities. Nanoparticles are expected to provide a localized cure for complex diseases by facilitating targeted delivery and improved bioavailability. The functionalized nanoparticles can either act as the vehicle for potent drugs or they themselves can act as the therapeutic agents. We have developed antibiotic conjugated carbon-based nanoparticle systems and the conjugated systems displayed notable antibiotic effects on various gram-negative bacteria, including those resistant to the antibiotic moiety conjugated onto the nanoparticle. The aim of this project is to extend these systems to include different antibiotic moieties to construct a range of effective antibiotic conjugate analogues onto the nanoparticles.

Material Science, Chemistry, Nanomaterials, Drug Delivery

ChemicalSciences

Despite its enormous scientific and technological success, quantum theory suffers from deeply puzzling conceptual problems, none more vexing than the quantum measurement problem. It involves inconsistencies that arise when considering the treatment of "observers" as physical systems amenable to a quantum description. Recent results on extended versions of the "Wigner's friend paradox" exemplify the measurement problem in the form of rigorous no-go theorems, such as the "Local Friendliness" no-go theorem. It shows that certain sets of a priori plausible assumptions cannot be simultaneously satisfied by any theory that can accommodate certain phenomena where an "observer" can be treated as ordinary systems subject to quantum-mechanical operations. This project, which has both a conceptual and a technical component, aims to propose increasingly convincing experimental realisations of such phenomena, by asking what are sufficient conditions for a system to be deemed an observer, and what experimentally feasible but increasingly sophisticated quantum systems may provide models of quantum-coherent observers.

Quantum Physics

PhysicalSciences

The 2022 Nobel prize in Physics was awarded to the experimental demonstration of quantum entanglement and its counterintuitive properties, in particular the violation of Bell inequalities. A modern way to understand this phenomenon is as a failure of a classical causal model that satisfies relativistic constraints on causal structure. The program of quantum causal models aims at resolving the puzzle of Bell's theorem by extending the classical framework of causality to a quantum setting, while maintaining compatibility with relativistic causal structure. This project will involve further developing the framework of quantum causal models and addressing various open questions, such as counterfactual reasoning, indefinite causal structure, and/or potential applications to quantum information processing tasks.

Quantum Physics

PhysicalSciences

Quantum technologies are poised to become major drivers of scientific and economic growth in the 21st century. On the other hand, quantum advantage over classical computers has only been demonstrated for a few classes of algorithms. This interdisciplinary project will tackle the key question for unlocking the benefits of quantum information processing: what gives quantum mechanics its information-processing power beyond classical physics? It will explore the hypothesis that quantum advantage is associated to fundamentally different ways in which causality operates in the quantum and classical regimes.

Quantum Physics

PhysicalSciences

This project centres around construction of simulation frameworks for a variety of high impact plasma and electron transport applications, such as atmospheric lightning discharges, low temperature plasma-solid interactions through to magnetically confined fusion plasmas. Areas of investigation can be tailored to candidate expertise & interests, including numerical solution techniques for transport equations, the closure problem, machine learning and AI in computational science, kinetic or Monte Carlo methods.

Applied Mathematics; Plasma Physics; Modelling

ComputationalScience

A variety of projects are available in different modelling areas, with the focus applied to modelling a variety of important physics scenarios important to tokamak plasmas, such as those anticipated in ITER. Equilibrium plasma discharge, tokamak disruption, runaway electrons, edge-plasma, and surface wall interaction applications are examples of focus applications.

Plasma Physics; Fusion; ITER

PhysicalSciences

The current wave of deep learning and AI research has yielded many advances in how tools such as neural networks, optimization, or uncertainty quantification can be used to improve modelling capability for a number of useful applications. Projects are available in the development of robust and transparent machine learning and AI techniques that can be employed to augment existing computational modelling techniques (e.g. surrogate models, reduced order models, etc) or to provide new avenues of solution (e.g. PINNs as a famous example).

Applied Mathematics; Deep Learning; Modelling

ComputationalScience

Photons are low-noise and flexible quantum systems, perfect for quantum communication and quantum information processing. However, to date, it has not been possible to create key photonic quantum states such as high-fidelity states of many correlated photons and complex heralded entangled photon states. These projects will use high-efficiency photon-pair sources developed at Griffith University and world-leading superconducting photon detectors to develop and generate these important photonic quantum states. 

Quantum Physics

PhysicalSciences

Quantum state smoothing is a newly developed way to estimate the state of a quantum system at time t using measurement results in both the past and future of t, with applications in experiments with continuous measurements. This project will further develop this formalism, including using it to address the question of what is the most likely thing a quantum system would have done if you had measured it in a different way from how you did. Feel free to contact me about other areas I have published in recently.

Quantum Physics

PhysicalSciences

The project will apply quantum machine learning to the problem of tracking the state of an open quantum system. Specifically, we want to find the most memory-efficient classical apparatus, which performs adaptive quantum measurements so as to maintain the state of the quantum system in a stochastically varying conditional pure state. While this problem can be attacked by exact methods in classical numerics, these are very computationally expensive, so machine learning is an obvious alternative. Most interestingly is to use genuine quantum machine learning. That is, to perform quantum machine learning experimentally, where the system itself is part of the machine learning loop. This project thus has an experimental quantum photonics supervisor also.

Quantum Physics

PhysicalSciences

This project is an investigation into the time that it takes for an electron to tunnel-ionise from molecules, referenced to the tunnelling time from atomic hydrogen The proposed research is based around a state-of-the-art laser system, the Australian Attosecond Science Facility (AASF). This laser system is unique in Australia and one of only a few around the world. The light pulses generated by this laser are highly amplified and are only a few cycles of the optical field, and so measured in attoseconds (10-18 sec). We study of the interaction of such strong-field engineered light pulses with matter. The research will build on a ground-breaking research into the time it takes for tunnel ionisation to occur in atomic hydrogen, which was recently published by the Griffith team in Nature [Sainadh et al. Nature, 568, 75 (2019). This project will extend the measurement of the tunnel ionisation of electrons from other atoms and molecules and will provide the most stringent tests to current models for these interactions.

Quantum Physics

PhysicalSciences

Atom interferometers have demonstrated great promise for next generation accelerometers and gyroscopes, with significant gains in sensitivity and immunity to bias drift . To date, most work has focused on pulsed atom interferometers, which use a series of time-seaparated light pulses to split and recombine the atomic ensemble, with the resulting phase shif. However, pulsed approaches suffer from significant loss in bandwidth, due to dead-time where no measurement is made. This project will construct a continuous beam interferometer using laser cooled rubidium atoms, with the interfereterometer sequence constructed by atoms traversing spatially separated light fields, giving significant gains in bandwidth and flux.

Quantum Physics

PhysicalSciences

Globally, the whale watching industry has been increasing in size and economic value since the 1990s. Whale-watching tourism has transformed entire local communities and contributed significantly to economies. The whale-watching industry, and the whales themselves, face uncertain threats from multiple pressures. This includes the impacts of increased sea surface temperature, altered currents and changes in food abundance on whale behaviour. The research project will look at adaptations of the whale watch industry to changing whale distributions and abundance, drawing from two primary species for Australian waters for which the Whales & Climate Program has data on climate change impacts. This study involves modelling, social and economic science, with a focus on sustainable tourism.

Eco-Tourism

EnvironmentalScience

Globally, the whale watching industry has been increasing in size and economic value since the 1990s, yet, little is known about the importance of this sector to the local economy. This research project aims to update and establish the latest figures on this sector for Australia recognising the increase of whale watching and swim with whales in Australia. The most recent estimates on the contribution of whale watching to the economy date back to 2008, where it was found that over 1.6 million people went whale watching, generating AUD $47 million in ticket expenditure and AUD $264 million in total tourism expenditure. This project will involve the analysis of historic customer number and revenue data collected by whale watch operators and may also involve collecting data directly from whale watch participants via an expenditure survey.

Eco-Tourism

EnvironmentalScience

The Broadwater on the Gold Coast is a large semi-enclosed tidal estuary that forms the southern portion of Moreton Bay. While the estuary receives fluvial sediments from four major river catchments, the dynamic coastal processes have long dominated sediment inflow into the estuary. The study seeks to quantify the ratio of catchment-derived to marine-derived sediments in the estuary, and determine refined proactive management mechanisms for maintaining ecosystem function and navigability within the estuary.

Environmental Science Management, Geochemistry

EnvironmentalScience

Governments have a key role to play in achieving sustainable development and addressing climate change. The object of this research is to synthesise policies, plans and strategies that will assist with this transition. While commitments have been made at the international level, and some organisations have made improvements at the local level, there is a strategic gap between the two that has not been fully researched. PhDs can be on either sustainability or climate change and take a theoretical or applied approach. The methods used include case studies, comparative analysis, policy analysis, stakeholder interviews, or surveys.

Environmental Management, Sustainability and Climate Change Adapatation

EnvironmentalScience

The project will assess the impacts of bushfires on water quality and biogeochemical processes within Eastern Australian waterways to better understand the short- and long-term impacts of bushfires on aquatic biogeochemical cycles in estuaries. The fate, transport, and cycling of target metals and nutrients (Fe, Mn, C, N, P, S) will be the focus of this study with both laboratory and field-based experiments utilised. This project is a collaboration between the NSW Government’s Estuaries and Catchment team based in Lidcombe NSW and Griffith University (Gold Coast campus) with opportunities to be based at either location. Focus areas are bushfire-related aquatic biogeochemistry, environmental pollution, and environmental chemistry.

Biogeochemistry; Environmental chemistry; Bushfire

EnvironmentalScience

Australia is home to large reserves of "critical minerals" - those metals that are essential to the transition to renewable technologies. Our knowledge of the environmental chemistry of these metals is currently limited, particularly in the coastal and marine waters that will likely be their ultimate sink. This project seeks to use advanced analytical approaches, including Synchrotron-radiation X-ray spectroscopy, to unravel the complex aquatic geochemistry of critical metals in coastal and marine environments.

Geochemistry, Environmental Chemistry, Marine Chemistry

EarthScience

There is currently a surge in interest in marine and coastal restoration, with a significant number of projects underway, and many more planned. Current methods for monitoring restoration progress and success vary enormously, with low uptake of technological advances that promote efficiency and comprehensiveness. This project will work towards a coordinated, open-science approach to monitoring, that standardises data formats, allows trade-offs or synergies between ecological, socio-economic and cultural benefits to be explored, and facilitates cross-project comparisons and benchmarking. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams.

Oceanography

EnvironmentalScience

Connectivity is a guiding principle for conservation planning, but due to challenges in quantifying connectivity, empirical data remain scarce. This project provides solutions to the challenge by using computer vision to automatically extract fish movement data from underwater camera streams. The student will develop expertise in fisheries ecology, statistical modelling and programming. The project takes advantage of Griffith’s leadership in automated monitoring of marine environments, including through computer vision on underwater camera streams. It will lead to better planning and management of marine restoration and protected area projects through incorporation of connectivity principles.

Oceanography

EnvironmentalScience

Previously we showed the enzyme dihydrolipoamide dehydrogenase (DLD) to be a metabolic master regulator. We now will characterise the role of DLD in the metabolic network of C. elegans by using metabolomics and biophysical techniques in isolated mitochondria, as well as curating the genome scale metabolic model of C. elegans in collaboration with the WormJam consortium and simulating the nematode’s metabolism.

Systems Biology

BiologicalScience

The metabolic interactions between the endosymbiont Wolbachia and its insect hosts depend on the combination of Wolbachia strain and host organism and range from mutualistic symbiosis to parasitic interactions. With a combination of metabolomics and physiological techniques we want to characterise these interactions and the role they play in hindering the transmission of insect-borne virus diseases.

Systems Biology

BiologicalScience

In collaboration with colleagues at QAAFI and other institutions we are using NMR-based metabolomics as analytical platform technology to characterise the composition of foods such as honey and native Australian fruits. This involves characterising the potential of native Australian fruits as commercial food sources and developing methods for the detection of food fraud especially in honey.

Food Analysis

BiologicalScience

Olfactory glial cell transplantation therapy is effective for repairing spinal cord injury, but the approach needs enhancing to improve outcomes. This project will determine the optimal combination of cell types needed to produce cellular nerve bridges for transplantation into the injury spinal cord. The project will develop new techniques for cell purification and three-dimensional cell nerve bridge production.

Biochemistry Cell Biology

BiologicalScience

Peripheral nerve injuries are devastating as they can result in permanent paralysis. This project will use drug discovery and cell transplantation approaches to develop therapies to treat peripheral nerve injuries in animal models. The interaction of the transplanted cells with the host nerve will be examined and the functional outcomes will be addressed using behavioural and electrophysiological studies.

Biochemistry Cell Biology

BiologicalScience

Pathogens such as bacteria and viruses are likely contributors to the onset and progression of Alzheimer’s disease. This project will use drug discovery to identify compounds that can stimulate glial cells of the nervous system to combat chronic pathogen infection of the brain. The project will use in vitro cell cultures and in vivo animal models of brain infection.

Biochemistry Cell Biology

BiologicalScience

Humans have utilised plants since the dawn of time for therapeutic purposes. Many important and well-known drugs (e.g. taxol, morphine) come from plants. Endemic Australian rainforest and desert plants have yielded many new and bioactive natural products, but remain under-investigated. This project will result in the purification and characterisation of new bioactive compounds, and that will impact biodiscovery.

Biochemistry Cell Biology

BiologicalScience

Natural products display chemical complexity and diversity and they inherently interact with biomolecules (e.g. proteins, DNA), making them an ideal source of unique scaffolds for screening library synthesis. This medicinal chemistry project will generate unique biodiscovery libraries that will be fully characterised using spectroscopic methods before being screened in anti-infective, anti-cancer, or ion channel functional assays.

Biochemistry Cell Biology

BiologicalScience

Nature provides unlimited inspiration for innovation in the pharmaceutical and agrochemical sector. The Nobel Prize-winning discovery of the anti-parasitic drugs avermectin and artemisinin has renewed interest in exploring natural products for new anti-infective drugs. This project will result in the identification, semi-synthesis and full characterisation of new molecules that display anti-viral, anti-microbial or anti-parasitic activity.

Biochemistry Cell Biology

BiologicalScience

Giardia parasites infect approximately 1 billion people and cause over 200 million cases of giardiasis each year. They also cause significant morbidity in animals. However, current treatments are inadequate, associated with resistance and collateral microbiota impacts. This project aims to improve the treatment of giardiasis by investigating the biological and pre-clinical activity of potent new anti-Giardia compounds in animal models of infection.

Biochemistry Cell Biology

BiologicalScience

Trichomoniasis is a neglected parasitic disease that causes significant morbidity in pregnant and elderly women (over 100 million infections each year). However, the only FDA approved therapy for this disease is associated with treatment failures and adverse effects. This project aims to develop and implement a new medium to high-throughput assay to identify and investigate new drug leads for trichomoniasis.

Biochemistry Cell Biology

BiologicalScience

The inclusion of probiotics in animal feeds have proven to be beneficial to animal health. This project is a collaborative research program between Griffith University and Bioproton, aiming at investigating the mechanism of action of probiotics that have antimicrobial activity. The outcome of the project will lead to scientific discovery on the antimicrobial behaviours of probiotics. The active Bacillus strains can be used as effective antimicrobial agent in animal feed materials.

Biochemistry Cell Biology

BiologicalScience

Many TCMs have a neuroprotective effect; that is, they protect the central nervous system against damage or degeneration due to diseases such as Parkinson’s disease. Working with TCMs with a known neuroprotective effect, we can isolate and identify the major constituents of selected TCM and test the compounds against cell-based models of Parkinson’s disease. By analysing and testing TCMs, we can determine their mechanism of action and develop new ways to treat neurological diseases.

Biochemistry Cell Biology

BiologicalScience

Neurological disorders such as schizophrenia and dementia are caused by a ‘perfect storm’ of unique combinations of genetic and environmental factors. Such complex combination of events leads to disruptions in gene networks and biological pathways that alter cell functions and consequently influence disease risk. New approaches in genomic technologies, computational models and experimental systems could potentially lead to personalised treatment based on an individual’s genetic composition. This project aims to map molecular networks and cell functions affected in patient-derived stem cells to help discover new therapeutic strategies tailored based on patient’s molecular and cellular signatures.

Biochemistry Cell Biology

BiologicalScience

This project aims to investigate the epigenetic regulation via microRNA gene silencing adopted by Epstein-Barr virus (EBV) to “hack” the genetic program of human B-lymphocytes (B-cells). We use a novel EBV/B-cell model system to characterise the functional role of viral microRNAs in the micro-management of cellular pathways associated with persistent B-cell infection. Our integrated platform will contribute to better understanding of fundamental molecular and cellular processes underpinning viral infection, immune escape and proliferation. The overarching goal is to produce a system-based platform to understand the mechanisms of epigenetic regulation by microRNA gene silencing associated with virus-host interactions and human cell infection.

Biochemistry Cell Biology

BiologicalScience

The cells of blood vessels produce sticky molecules called proteoglycans and once modified can bind and retain cholesterol. Zebrafish express all the major receptors, lipoproteins and enzymes involved in atherosclerosis and a complete set of genes to proteoglycan synthesis and modification. This project will develop a high-fat diet-induced zebrafish model of atherosclerosis to allow for screening of potential vessel wall directed therapies to prevent cholesterol binding.

Cell biology, Biochemistry, signal transduction

BiologicalScience

Well defined risk factors such as high cholesterol, smoking, and high blood pressure worsen the burden of atherosclerosis. Patients with inflammatory bowel disease (IBD) present with a lower prevalence of classic risk factors, however, have at least a 2-fold higher risk of heart disease. Elevated inflammatory cytokines and an altered microbiome are observed in patients with IBD. This project seeks to define the biological link between IBD and heart disease by assessing the role of inflammatory cytokines and bacteria-derived toxins on vascular cells.

Cell biology, Biochemistry, signal transduction

BiologicalScience

Virus-like particles are non-infectious mimics of viruses that can often enter cells via the same receptor-mediated pathways as the viruses they resemble. Our work in this area includes the development of fluorescent analogues of important human pathogens and the creation of particles of different shape and size to understand the fundamentals of virus-cell interactions.

Biochemistry Cell Biology

BiologicalScience

We have developed a number of virus-derived protein cages into robust containers for enzymes. In addition, we are constructing hybrid biomaterials with properties tailored to working with different classes of small molecules. There are a number of project opportunities on the application of biocatalytic protein cages in drug discovery and metabolism.

Biochemistry Cell Biology

BiologicalScience

We have determined the first structure of a persistent plant virus. It is not clear what advantage these asymptomatic viruses confer in order to maintain the purported symbiotic relationship they have with their hosts. Understanding the form and function of persistent viruses through molecular and structural biology will open many possibilities for their use in plant biotechnology.

Biochemistry Cell Biology

BiologicalScience

This project aims at establishing new zebrafish models of motoneuron degeneration or neurodegeneration per se. We will use state-of-the-art genome editing tools (optimised CRISPR/Cas9 approach) to manipulate selected genes of interest to both validate their predicted pathogenicity and generate animals developing neurodegeneration. These models will further be used to investigate the underlying degenerative mechanisms and establish drug screening/discovery programs.

Biochemistry Cell Biology

BiologicalScience

Whilst many current drugs are derived from nature, many more bioactive molecules have still to be discovered. To help speed up discovery, we will develop i) a unique multipurpose zebrafish model combining different transgenic fluorescent markers/sensors and ii) automated assays to screen existing diverse chemical libraries for bioactive molecules. Validated assays will then be used to screen natural product libraries and start looking for the drugs of tomorrow.

Biochemistry Cell Biology

BiologicalScience

Neurexins are a family of genes that have been associated with several neurological diseases. We have generated a series of innovative zebrafish CRISPR-mutants that should allow to better understand the role of these genes in the developing brain. We will combine single-cell transcriptomics studies, high-end imaging, and behavioural approaches to highlight their critical function in brain development and plasticity.

Biochemistry Cell Biology

BiologicalScience

Parkinson's disease (PD) is an ageing-related, multifactorial neurological disorder featuring selective degeneration of dopaminergic neurons in the midbrain. The mechanism underlying the loss of dopaminergic neurons is complex and still elusive. However, ion channels have been shown to play an important role in neurodegeneration due to their fundamental functions in neuronal excitability. In this project, we aim to expand our recent findings on the potential pathological role of potassium channels in Parkinson's disease, and elucidate the underlying molecular mechanisms using a dopaminergic neuron cell model and knockout mouse model.

Molecular biology, Biochemistry, Cell Biology, electrophysiology

BiologicalScience

Microglial cells, the CNS-resident macrophages, are privileged to be the immune-competent cells of the central nervous system. A large body of evidence supports that microglial cells play a crucial role in mediating neuroinflammation as a significant contributing factor in the progression of ageing-related neurological disorders, including Parkinson's disease (PD). As an essential trigger of abnormal microglial activation, oxidative stress is also a key pathological factor in PD. This project aims to explore how oxidative stress-sensitive ion channels contribute to microglia-mediated neuroinflammation in PD and whether targeting these ion channels may represent a neuroprotective approach to mitigate PD progression.

Molecular biology, Biochemistry, Cell Biology, electrophysiology

BiologicalScience

This project combines advanced protein engineering with materials science and biotechnology. Sensitive and specific detection of serum antibodies is often used to diagnose infections. This project aims to develop a simple qualitative/quantitative device for detection of antibodies of interest. It will involve protein engineering of protein switches to incorporate antigens while attached to biomolecular scaffolds. Binding of the antibodies to the antigens will activate the protein switch which will result in release of a signal.

Biochemistry Cell Biology

BiologicalScience

This project harnesses the biosynthesis capacity of microbial cells to produce polymeric self-assemblies that can be engineered to incorporate protein functions such as antigen, binding domains and enzymes. This approach uses metabolic engineering and protein engineering to exploit the vast biomaterials design space for generation of innovative smart materials that form core-shell structures and exhibit advantageous properties toward such as uses as antigen carrier in vaccine applications or for targeted delivery of active compounds.

Biochemistry Cell Biology

BiologicalScience

About 15% of lung cancer patients survive beyond 5-years. CT screening to early detect lung nodules has been investigated, however false positive results, unnecessary radiation exposure are some of the drawbacks. We propose an innovative approach to identify nodules found on CT scans using breath analysis and liquid biopsies. This new multidisciplinary partnership will lay the foundation for future collaborations.

Cancer research, Liquid biopsy and salivary diagnosics

BiologicalScience

Oropharyngeal cancer (OPC) caused by human papillomavirus (HPV) is rapidly increasing globally, with an estimated 173,495 new cases in 2018. Approximately ~10-25% of patients develop recurrences within 2-years. The aim of this NHMRC funded project is to develop a microfluidic chip to permit capture of high-purity and viable circulating tumour cells (CTCs) to early detect recurrences in HPV driven OPC.

Cancer research, Liquid biopsy and salivary diagnosics

BiologicalScience

Glioblastoma (GBM) is the most frequent and aggressive form of brain cancer in adults. Currently, there are no biomarkers to reliably evaluate disease progression during treatment, leading to delays in important clinical interventions. To improve noninvasive monitoring of cancer and find new potential targets for therapies, liquid biopsy approaches, including the use of extracellular vesicles (EVs), circulating tumour cells (CTCs) and circulating tumour DNA are being investigated. The liquid biopsy approach has advantages over tumour tissue biopsy since it allows serial timepoints collections and in a minimally invasive way. We aim to expand results obtained on EVs, ctDNA and CTCs isolated from blood and saliva of GBM patients, validating them in larger cohorts and identifying novel biomarkers to help in the diagnosis and prognosis of this disease.

Cancer research, Liquid biopsy and salivary diagnosics

GRIDD

Heart failure is a major global pandemic affecting more than 38 million people worldwide. It has been suggested that poor oral hygiene and periodontal diseases are related to a higher risk of developing cardiovascular disease. However, the underlying cause of this phenomenon has not yet been investigated. We are aiming to profile the oral microbiome content in patients with heart failure

Cardiovascualr disease, biomarkers and biosensors

BiologicalScience

The spread of cancer (metastasis) accounts for 90% of cancer deaths. Critically, this belligerent disease is highly resistant to conventional therapies, and new molecular targets and therapeutic avenues are urgently needed. Professor Richardson discovered innovative anti-cancer drugs that can increase the expression of a metastasis suppressor protein, NDRG1, that prevents tumour cell spread (Fig. 1). He also discovered these same drugs overcome resistance of cancers to chemotherapies by overcoming the drug efflux pump, P-glycoprotein. This project will involve examining the functions of NDRG1 and its targeting by our novel drugs to elucidate the molecular mechanisms involved in their anti-tumour activity. A range of state-of-the-art techniques will be used to maximise student training, including: tissue culture, western blot analysis, immunohistochemistry, medicinal chemistry, and confocal microscopy.

Biochemistry Cell Biology

BiologicalScience

Despite the massive potential of pharmacologically harnessing the power of the macrophage (MØ), a lack of understanding basic molecular mechanisms led to a distinct absence of MØ-based anti-cancer therapies. MØs are powerful orchestrators of the response to tumours, making up to 50% of tumour mass. The MØ powerfully exerts tumour inhibition via either cytotoxic M1-MØs, or tumour promotion via the M2-MØ phenotype. However, a unifying model of how this occurs via nitric oxide (NO) has never been elucidated. Using our expertise in exploiting transporter pharmacology to develop innovative drugs from bench-to-bedside, we will assess the transporter, multidrug resistance-associated protein 1 (MRP1), to exploit NO transport between MØs and tumour cells (Figure 2) to develop frontier drugs (“MACA-ATTACKERS”) to harness the immense power of the MØ.

Biochemistry Cell Biology

BiologicalScience

Parkinson’s disease (PD) is a complex, incurable, multifactorial neurological condition affecting over 65,000 Australians with an economic burden of $10 billion per annum. With an aging population the disease related costs will rise unless we find better ways to identify those at risk, provide early diagnosis and treat the disease from an understanding of its causation in each individual. The development of robust biomarkers is essential to meeting these challenges. No biomarkers are available which is the major impediment to progress towards a cure. We have developed a cell model of PF using patients’ own cells. Subjecting the cells to chemical stress reveals a different response between cells from PD patients and those from healthy individuals. We have several projects examining how we can use these stress tests to identify the underlying disease trigger in each patient. This is the first step toward personalised medicine for PD.

Biochemistry Cell Biology

BiologicalScience

Genetic factors constitute a major component in the aetiology of Parkinson's disease (PD). Significant progress towards understanding the pathologic mechanisms involved in PD and developing new therapeutics has come from studies of rare families with inherited PD. We hold an advantaged position in this research field via access to the unique cohort of thousands of PD patients participating in the Queensland Parkinson’s Project. Through sophisticated genetic studies, we have identified several novel genes from rare PD families, the encoded proteins of which have great potential in elucidating new pathologic mechanisms and providing novel treatment strategies. Using methods in molecular biology, cell biology, biochemistry and stem cell biology, we aim to shed new light on this progressive and devastating disease.

Biochemistry Cell Biology

BiologicalScience

We have made the exciting discovery that the clinically used antimalarial drug proguanil has much more potent activity than previously thought. The activity of proguanil has, up until now, been thought to be due to its in vivo cyclization metabolite cycloguanil, a DHFR inhibitor, and by potentiating atovaquone activity. In this project, cyclization blocked analogues of proguanil, will be investigated as potential new combination partners for atovaquone. Approaches will include in vitro growth inhibition assays, combination studies, time of kill assay and in vivo efficacy testing in murine models of malaria.

Infectious Diseases

BiologicalScience

This project aims to develop native mass spectrometry methods for characterising challenging and unconventional targets that underpin emerging disease therapeutics. Native mass spectrometry is a rapidly growing biophysical technique – this project is one of few opportunities in Australia to develop skills with this emerging and continually developing methodology. Potential biomolecular targets to be investigated include soluble and membrane proteins and structured RNAs, and their complexes with other proteins, nucleic acids and/or lipid binding partners. Development of these methods will facilitate the fundamental understanding of these molecules and further drug discovery by allowing fragment, or other, screening campaigns to discover novel binding compounds, or characterise previously identified therapeutic binding compounds. This can be applied to various diseases areas including cancer and infectious diseases

Chemical Biology

ChemicalSciences

The Molecular Targets Program identifies ligands for any cloned and purified protein of therapeutic significance. We have developed native Magnetic Resonance Mass Spectrometry (MRMS) to fast track identification of compounds that can be used for therapy. As viruses contain very few proteins, this platform allows rapid response to viral pandemics. e.g. the discovery of anti-COVID19 anti-virals.

Biochemistry Cell Biology

BiologicalScience

A novel approach called PROteolysis TArgeting Chimera (PROTAC) involves the development of bifunctional hybrid molecules that enable the target protein to be ubiquitinated to promote proteasomal degradation. We aim to develop the first native mass spectrometry-based platform that offers direct characterisation of ternary complex formation, population, stability, binding affinities, cooperativity, or kinetics by PROTACs and overexpressed proteins (both target proteins and ligases) in cells, overcoming the need for purification.

Biochemistry Cell Biology

BiologicalScience

Target identification is crucial for rational drug design and is a current bottleneck for advancing bioactive compounds through the discovery pipeline. This project will use the power of native mass spectrometry to establish and validate a disruptive new platform to elucidate targets of bioactive compounds by direct detection of protein-small molecule binding. The proposed approach will accelerate current target identification as it will not need to modify bioactive compounds or proteins to achieve this outcome.

Biochemistry Cell Biology

BiologicalScience

Major membrane protein (MP) functional groups such as the GPCRs and ion channels, alone make up approximately 56% of protein drug targets. This project aims to develop a universally applicable MS-based label free method that allows rapid screen for MPs. This project will revolutionise and dramatically accelerate the drug discovery process by developing new native MS-based tools that can find novel therapeutic ligands that target MPs.

Biochemistry Cell Biology

BiologicalScience

This project aims to develop new approaches for the identification of novel natural products (purified or within a fraction library) interacting with known (target-biased strategy) or unknown (unbiased strategy) protein targets based on advanced mass spectrometry (MS) techniques. An innovative and powerful chemical biology platform will be established to enable direct and rapid discovery of natural product-based probes and their protein targets. This project detects NP-protein interactions directly from cell lysates treated with natural product extracts/fractions/compounds.

Biochemistry Cell Biology

BiologicalScience

Micro-technologies in the form of Micro-Electro-Mechanical Systems (MEMS) and micro-plasmonics platforms offer the potential for high-resolution, high-throughput label-free sensing of biological and chemical analytes. Silicon carbide (SiC) is an ideal material for augmenting both MEMS and plasmonics routes, however such inorganic surfaces need to appropriately and efficiently functionalised to allow subsequent immobilisation of functional biomolecules. To this end we trialled various organosilane-based self-assembled monolayers for the covalent functionalisation of 2-dimensional SiC films, and have now developed an affordable, facile one-step method. Using high-throughput glycan arrays as our model system a novel platform that has the potential to combine established array technology with the label-free capabilities of MEMS or plasmonic systems is one step closer. Using a similar functionalisation route, we have extended the use of organosilanes to biofunctionalise the surface of 3-dimensional nanoparticles, specifically carbon dots. Carbon dots are cheap, biocompatible, chemically stable, heavy-metal free quantum dots, of low toxicity that offer an alternative approach for bio-imaging and -sensing applications. Again, employing glycans as our model system, we are now using our biofunctionalisation approach to generate glycan-coated carbon dots that we are using to explore complex glyco-interactions.

Micro- and Nanotechnology, Bioscience, Chemistry

BiologicalScience

Mushrooms are increasingly attracting attention for their immuno-modulatory activities, which are primarily due to beta-glucans. beta-Glucans comprise a group of glucose (Glc) polysaccharides that are chemically diverse, with a common b-glucan being cellulose (b-(1,4)-linked Glc. It is non-cellulosic beta-glucans, mainly beta-(1,3)-linked Glc that have been shown to be potent immunological stimulators in humans, and some are now used clinically in China and Japan, as well as being commercially available in Australia. Due to the complexity of beta-glucan chemistry and structure a detailed understanding of the mechanism of action, specifically the structural components that dictate specific immunological responses, are yet to be fully resolved. In collaboration with Integria Healthcare, the overall objective of the project is to explore the immuno-modulatory effects of mushroom beta-glucans, specifically the project aims to structurally characterise commercially available mushroom polysaccharides rich in beta-glucans and correlate this with their associated immuno-modulatory effects The outcomes from this project will lead to a clearer understanding of the properties of beta-glucans associated with commercially available mushroom polysaccharides that induce specific immuno-modulatory effects.

Biochemistry, Chemistry, Cell Biology, Immunology

BiologicalScience

The opportunistic human pathogenic fungus Aspergillus fumigatus causes severe systemic infections including Invasive Aspergillosis (IA), a major cause of life-threatening fungal infections in immuno-compromised patients. An over-whelming number of reports appeared in 2020 demonstrating that COVID-19-associated pulmonary Aspergillosis (CAPA) is one of the leading factors affecting morbidity in critically ill COVID-19 patients with some reports even classifying Aspergillosis as a significantly under-recognized ‘Superinfection’ in COVID-19. Drug resistance among fungal pathogens is continuing to develop into an increasingly serious threat to public health and health-care systems worldwide. This PhD projects entails the development of novel antifungal therapies that are urgently needed using our established and unique combined in-silico/SPR drug discovery pipeline evaluating a number of new protein targets.

Biochemistry, Microbiology, Drug Discovery

BiologicalScience

Antimicrobial resistance to commonly prescribed antibiotics remains an ongoing global threat. This project will develop theranostic nanomaterials that overcomes antimicrobial resistance and allows both diagnosis and stimuli-responsive treatment of infectious diseases in one dose. The outcome of the project will open a whole new way to manage and treat infectious diseases.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Cancer is a major cause of illness in Australia and has a substantial social and economic impact on individuals, families and the community. Although technologies continue to evolve, currently, most cancers could not be completely cured. This project will explore different innovative ways involving thrombosis for more effective treatment of cancers

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Vascular calcification is an actively-regulated process mediated by vascular smooth muscle cells where calcium phosphate crystallizes in the form of apatite, predominately depositing in the vascular tissues. Vascular calcification is one of the predictors of cardiovascular disease and can lead to cardiovascular dysfunction. This project aims to develop novel biomimetic-functional nanomaterials for targeted treatment of vascular calcification. This intelligent material is expected to specifically reach VC site where it releases a local anti-calcification activity, which could minimise off-target side effect and enhance therapeutic capability with minimal administered dose.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

This project focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. The primary goal of our research is to advance the diagnosis and treatment of life-threatening diseases such as cardiovascular diseases, cancers and blood disorders with the help of nanotechnology and microfluidics.

Micro- and Nanotechnology, Bioscience

Technology

This research focuses on multidisciplinary research at the interface between chemistry, nanotechnology, biology and medicine. Research at this interface has the potential to generate breakthroughs in fundamental science as well as lead to advanced technologies for diagnosing, monitoring and treating disease. Current (selective) research projects are the following: Point-of-care (POC) diagnostics; microfluidic methods for the detection of cancer; portable devices for cancer epigenetics; nanomachines for exosome and exosomal biomarkers detection; and superparamagnetic materials in biosensing applications.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Despite the tremendous efforts in developing effective on-site biosecurity and best management practices, waterborne parasites still cause significant health and economic burden worldwide. Early and rapid diagnosis together with an understanding of disease severity is critical for preventing parasite spread and enabling effective management strategies. Current routine diagnostic tests for waterborne parasites are not suitable for on-site detection. Shiddiky Laboratory is working on developing novel biosensing platform devices for the quantification and genotyping of waterborne parasites in surface and recreational waters. The device can be used to ensure improved waterborne parasite management, risk prediction, and rapid mitigation of impending outbreaks.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Exosomes are nanoscale (≈30–150 nm) extracellular vesicles of endocytic origin that are shed by most types of cells and circulate in bodily fluids. Exosomes carry a specific composition of proteins, lipids, RNA, and DNA and can work as cargo to transfer this information to recipient cells. Recent studies on exosomes have shown that they play an important role in various biological processes, such as intercellular signaling, coagulation, inflammation, and cellular homeostasis. These functional roles are attributed to their ability to transfer RNA, proteins, enzymes, and lipids, thereby affecting the physiological and pathological conditions in various diseases, including cancer and neurodegenerative, infectious, and autoimmune diseases (e.g., cancer initiation, progression, and metastasis). Due to their unique composition, easy accessibility and capability of representing their parental cells, exosomes, and exosome containing RNAs, proteins draw much attention as promising biomarkers for screening, diagnosis and prognosis of these diseases via non-invasive or minimally invasive procedures. Therefore, isolation and analysis of tumor-derived exosomes and exosome containing biomarkers at the early stage of the diseases could significantly improve the capacity to diagnose the diseases thereby improving outcomes. The Shiddiky group is pursuing studies of the development of multifunctional magnetic nanomaterials based technologies and devices for the highly selective isolation and sensitive detection of exosome and exosomal biomarkers (mRNA, proteins) in patients with cancer and other disease.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free diagnostic method that can detect diseases at the time and place of patient care (i.e., point-of-care) using minimal specialised infrastructure, has the potential to transform health care to many millions people both in the developed and developing countries. Recent advances in sequencing and proteomics technologies have now given rise to a large number of potentially useful genetic, epigenetic and other novel molecular biomarkers for the development of diagnostic methods for many diseases including cancer, infectious and neurodegenerative diseases. Despite these great input from biomedical engineering, significant technical challenges for achieving a functional POC device are yet to be overcome. This is mainly due to the lack of sensitive, specific, rapid and low-cost readout methods. The Shiddiky group is pursuing studies of improving existing and developing entirely new methods that can rapidly detect cancer, infectious and neurodegenerative diseases.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Plant pathogens reduce global crop productivity by up to 40% per annum, causing enormous economic loss and potential environmental effects from chemical management practices. Thus, early diagnosis and quantitation of the causal pathogen species for accurate and timely disease control and management are crucial. Detecting and quantifying pathogen species and their relevant genetic biomarkers in plant extracts at the early stages of the diseases is notoriously difficult to access via conventional methodologies. This is mainly because they are either too slow to enable efficient intervention and application of fungicides (visual observation of symptoms in the field) or are too expensive and technically complex to be used by non-specialized technicians on an industrial scale. The development of an affordable, sensitive, specific, user-friendly, rapid and equipment-free method for broad-scale disease surveillance in crop plants, based on “on-farm” pathogen detection and quantification, is of great interest to the agricultural industry and plant biology. The Shiddiky Laboratory focus to develop portable devices and technologies for ‘on-farm’ analysis of pathogen species and pathogenic biomarkers in unprocessed plant extracts. Such a device would allow more rapid and cost-effective detection, control and management of the plant diseases.

Technology, Chemistry, Micro- and Nanotechnology

BiologicalScience

Computational science and engineering is a modern approach to research, distinct from standard theoretical and experimental approaches. In computational research, fast computers are used to simulate or model the behaviour of physical systems to better understanding properties too difficult or expensive to study via experiments. Nano- and micro-scaled systems can pose a particular challenge for conventional experiments and theory, and are a natural fit for computational study.

Micro- and Nanotechnology

ComputationalScience

The future of scientific advancement will certainly involve a mixture of computational prediction and experiment. This interdisciplinary research theme promises to develop next generation theories for better modelling of chemistry, using novel mathematical and physical models. Suits students with a strong interest in applied mathematics and computers. Machine learning techniques will likely feature in this project.

applied mathematics, physics

MathSciences

Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems. In order to develop new strategic micro- and nanotechnologies, open questions on the behaviour of very small systems need to be addressed. Micro- and nanoscale systems exhibit unique properties that can’t be predicted from the theory of large-scale systems.

Micro- and Nanotechnology

ComputationalScience

This project aims to develop non-wetting droplets that can be used for chemical reactions and cell culture. These non-wetting droplets, known as liquid marbles, act as standalone miniature reactors that can be manipulated by external stimuli. This project focuses on using liquid marbles as building blocks for an integrated digital reactor platform that substantially improves reaction rates. You will work with research or commercial users to explore novel solutions as well as design and build innovative devices.

Physical Sciences, Chemical Engineering, Micro- and Nanotechnology

Engineering

Nanobubble technologies have applications in wastewater treatment, surface cleaning, sanitization, and therapeutics towards some age-associated diseases. This project focus on fundamentals of nanobubbles by exploring the stability, nucleation and dynamics of nanobubbles. The project aims to develop technologies to generate nanobubbles and apply the gained nanobubble technologies in agriculture to treat biomass, in poultry and dairy to achieve fast promotion of animal growth, in aquaculture to increase productivity and feed-conversion ratio with short harvesting cycle, in catalyst chemistry for renewable energy, and in therapeutics to treat diseases

Nanotechnology, Chemical Engineering, and Biomedical Engineering

PhysicalSciences

This project aims to develop highly efficient and stable semitransparent perovskite solar cells for innovative smart solar windows. The key concept is to explore novel functionalisation strategies on emerging carbon and two-dimensional materials to fabricate semitransparent perovskite solar cells for self-powered smart photovoltaic windows. Expected outcomes of this project include not only placing Australia at the forefront of research in the fields of materials science and renewable energy, but also creating commercial opportunities in Australia. This project expects to have various benefits for Australians – through the development of a cutting-edge sustainable energy device and the establishment of strong international collaborations.

PhysicalSciences, ChemicalSciences, and Nanomaterials

ChemicalSciences

This project aims to engineer a highly versatile micropatterned surface that can be used to culture and study cells.

Biomedical Engineering, Micro- and Nanotechnology, Fluid mechanics, Oragn-on-a-chip

Engineering

Developing and applying nanotechnologies to deliver solutions to forensic problems. Broadly speaking, these activities seek to apply materials science in a forensic context. Key areas of focus include: new fingermark development strategies; improving the specificity of presumptive testing for drugs of abuse; and assessing new and overlooked classes of evidence. Key themes include: safer, greener forensic processes; delivering new functionality or clearer interpretation; and interdisciplinary, practitioner-informed research.

Micro- and Nanotechnology

ForensicScience

This project will explore hosting three of the most ubiquitous chemical developers for fingerprints within cavitands so as to modify their solubility. The aim will be to make those developers water-soluble, thereby allowing the elimination of costly and environmentally damaging solvents from common forensic treatments.

Applied Chemistry and Forensic Science

ChemicalSciences

Using full-spectrum photographic equipment available at Griffith University, this synthetic project will develop new chemical dyes, stains, and fluorophores, that primarily emit in the UV and IR regions of the electromagnetic spectrum. These emissive treatments will then be trialled and validated against existing forensic treatments.

Applied Chemistry and Forensic Science

ChemicalSciences

This project aims to exploit high-performance, durable and cost-effective defective electrocatalysts for fuel cell and water splitting applications. It expects to generate a new area of knowledge to understand the interfacial phenomena of electrocatalysis and of how to develop technologies for the controllable synthesis of low-cost and highly efficient electrocatalysts. The expected outcomes of this project include a process for the development of cost-effective electrocatalysts thereby making hydrogen fuel cells and water splitting techniques economically competitive.

Electrochemistry and Energy Materials

ChemicalSciences

Microfluidics is both the science that studies the behaviour of fluids through microchannels and the technology of manufacturing microminiaturized devices containing chambers and tunnels where fluids flow or are confined. The previous works use rigid materials to construct the devices, and the device's functionality is mainly based on single physics. This project will design and develop a cutting-edge microfluidic technology by exploiting multiple physical coupling and flexible materials to achieve a variety of functions such as micropumps, micromixers, cell sorters, trappers etc. This technology will be applied in the lab-on-a-chip system for disease diagnosis, prognosis and treatment.

Micro- and Nanotechnology

Technology

This project focuses on understanding how nanostructures affect electrochemical reactions. More specifically, this project aims to understand how electrochemistry in nanoconfinement affects Li ion transport to improve the performance of Li-batteries

phycical-chemistry, electrochemistry, nanomaterials

ChemicalSciences

Double emulsions, referring to droplets of the disperse phase containing even smaller ones, are highly desirable for applications in drug delivery, food science, release of substances, etc., due to the embedded structure which can encapsulate different types of molecules. This project aims to produce and control high-throughput double emulsions using microfluidics.

Micro- and Nanotechnology, Fluid Mechanics

Engineering

When metals absorb atomic hydrogen from molecular hydrogen gas, first a disordered solid solution and then an ordered concentrated hydride phase are formed, with evolution of heat (enthalpy). The entropy and enthalpy changes are fundamentally linked through statistical mechanics. The goal of this PhD project is to control the enthalpy of the hydridring reaction by controlling the entropy change, with relevance to hydrogen storage (low enthalpy is desirable) and metal-hydride hydrogen compressors (high enthalpy is desirable). This would desirably involve both theoretical modelling using Density Functional Theory/Calphad to explore the possibilities of designing alloys such that the solid-solution phase has significantly higher/lower configurational entropy than the concentrated hydride phase, and experiments to make small amounts of alloys and measuring their hydrogen uptake properties in the National Hydrogen Materials Reference Facility within QMNC).

Condensed Matter and Materials Physics

PhysicalSciences

Today's room-temperature superconductors are all metallic hydrides. Superconducting transition temperatures (Tc) now reaching, even exceeding, room temperature are however observed only under extreme pressure, above 1 million atmospheres. Palladium hydrides - PdH, PdD and PdT - have been known to superconduct below about 10 kelvin for 50 years. It was recently found that PdH and PdD can become superconducting after absorbing hydrogen at a temperature and pressure above the thermodynamic critical point of the Pd - H2/D2 system, with superconducting transition temperatures reliably reaching 60 kelvin in PdDx (where x is not yet known). While this is low compared to room temperature, only low hydrogen pressures are required. The goal of this PhD project is to conduct cryogenic experiments to measure Tc of PdDx by by means of resistivity and alternating susceptibility simultaneously, under various experimental conditions, and to thereby understand how to obtain the highest possible Tc for this system.

Condensed Matter Physics

PhysicalSciences

Three-quarters of the periodic table is metals, and essentially all of these can be made to absorb hydrogen to form "alloys" (e.g. PdH) and compounds (e.g. MgH2). Thousands of metallic alloys also absorb hydrogen. In very many cases, hydrogen first forms a dilute solid solution which, as the hydrogen gas pressure increases, becomes unstable and a phase transformation to a concentrated hydride phase occurs, up to the thermodynamic critical point of the system. The goal of this PhD project is to investigate some new ideas about hydrogen uptake by metals. Two of these are: (i) Recent theoretical work based on Density Functional Theory proposes that in nanoparticles the system can transform via a single phase even below the critical point. This published result is controversial and has not been proved experimentally. This proposed phenomenon will be investigated in the Pd - D2 system by measuring hydrogen uptake while performing neutron diffraction (at the Australian Neutron Scattering Centre in Sydney) to determine what phases are present. (ii) Recent analysis based on statistical mechanics predicts that the assumed linear relationship between log(absorption pressure) and reciprocal temperature (the van 't Hoff relation) is in fact curved at high pressure, which matters for applications such as hetal-hydride hydrogen compressors for vehicle filling stations. This published result will be tested by measurements of hydrogen uptake by alloys at pressures up to 2000 atmospheres.

Condensed Matter Physics, Statistical Physics

PhysicalSciences

The transition to a global energy economy based on renewables is extremely urgent and underway. Hydrogen energy technology has a very imprtant role to play. For instance, it is estimated that the electric power required to produce and export enough hydrogen to satisfy just Japan's needs is more than 1 terrawatt. This compares to Australia's National Electricity Market which peaks at about 30 gigawatts, but also to our readily accessible resource of offshore wind at more than 2 terrawatts. Reliable computer modelling of such energy systems is vitally important in order to know how they will perform before capital investment is made; modelling is much cheaper than building even a pilot-scale system and getting it wrong. The consequences of design mistakes at the gigawatt scale are serious indeed. In this multidisciplinary PhD project, the student will work as part of a team with expertise in physics, mathematics, engineering, energy economics and hydrogen energy technology. The goal is to develop mathematical models and build software to analyse and simulate devices including electrolysers and fuel cells, and to link these into system-wide models for optimising an energy system according to mutually conflicting criteria such as best performance, minimum cost, least environmental harm and acceptable social licence. Optimisation will be by means of bio-inspired algorithms such as Particle Swarm and Grey Wolf.

Physics, Mathematics, Computing

MathSciences

This project combines artificial intelligence with molecular simulation to develop a predictive understanding of electrolyte solutions for developing the next generation of electrolytes for improved battery technology. Electrolyte solutions are one of the most important substances on earth, playing a central role in energy storage, carbon capture and conversion and essentially all of biology. Until recently, however, we have been unable to predict even their most basic properties. Recent advances in the field of AI and molecular simulation mean that for the first time it is possible to accurately predict their properties with existing software. This project will train students in the exciting and rapidly growing area of artificial intelligence for materials science. Many companies including Microsoft, DeepMind, Google Research and Schrodinger are currently investing in this area.

physical chemistry, chemical physics, artificial intelligence, materials science

ComputationalScience

This project aims to design and develop functional nanomaterials and nanocomposites for high-performance wearable energy storage devices (WESDs). A functional materials approach, together with precise control of device architecture through multi-materials/techniques additive manufacturing will be used to achieve maximum device performance with the required mechanical properties. The expected outcomes of this project include a detailed understanding of materials and devices structural-property relationship and the establishment of the fundamental principles on the microfabrication of flexible energy storage devices to support the burgeoning field of wearable devices, thus advancing the field of materials chemistry and advanced manufacturing.

Advanced Manufacturing

Engineering

This project aims to produce value-added functional 2D nanomaterials by advancing the green, scalable and costeffective electrochemical production method developed by the candidate. In addition to developing transformational electrochemical engineering technology to utilise Australian raw resources, this project will generate new knowledge in the area of materials chemistry and innovative additive manufacturing technology. Expected outcomes of this project include improved pilot-scale electrochemical reactors for producing various functional 2D nanomaterials and enabling precise control of their molecular and bulk properties. These tailored 2D nanomaterials will significantly improve the performances of flexible and energy-related devices.

Physical Chemistry

ChemicalSciences

Macular degeneration causes devastating visual impairment in the Australian population, and without new and effective treatment options, one in seven Australians with early signs of macular degeneration will likely progress towards advanced stages of the disease. The earliest known pathophysiological event to occur in macular degeneration is choroidal vascular dropout, and little is known about the events that occur prior to this microvasculature dysfunction and the contribution of the surrounding choroidal stroma and its resident cell populations. This project aims to construct a multicellular bioengineered choroid to elucidate deeper understanding about this specialized sensory support tissue.

Bioengineering, Biomedical Science, Cell Biology

BiologicalScience

Wildlife are in peril due to numerous threatening processes. Amphibians (especially frogs) are the most endangered Class of Veterbrates. Two key threats to amphibians are disease and environmental contaminants. The main disesae of concern is the fungal disease chytridiomycosis (pronounced 'ki-tri-di-o-my-co-sis'). This disease is caused by two related fungi: Batrachochytrium dendrobatidis and B. salamandrivorans. It is the most devastating disease threat to biodiversity ever recorded. To date it has caused the decline and/or extinction of hundreds of frog species around the world. Another key threat to amphibians (and other aquatic fauna) are environmental contaminants (including pesticides, heavy metals, firefighting chemicals, etc.). I have PhD opportunities available to study (1) infection dynamics of chytridiomycosis in frogs, exploring mechanisms of resistance and tolerance to the disease; and (2) independent and interactive effects of multiple threats to frogs, including the disease chytridiomycosis, and environmental contaminants.

Wildlife disease and environmental contaminants

EnvironmentalScience

Human activities introduce a huge diversity of pollutants into the environment, often with harmful consequences for wildlife. These pollutants frequently overlap, but many knowledge gaps exist when it comes to predicting their combined risks. Light pollution and pharmaceutical anti-depressants are two of the fasting growing stressors globally and have both been shown to negatively impact aquatic animals, but there has been no research exploring their interactive effects. This project aims to investigate the combined risks of light pollution and anti-depressant pharmaceuticals on the regulation of circadian systems, at multiple levels of biological organisation. The outcomes are expected to yield a new framework for exploring the interactive effects of chemical and non-chemical stressors and to reveal how non-chemical circadian entrainment cues such as light pollution modulate chemical toxicity.

Animal behaviour and physiology

Environmental Science

Gullies are the majority source of sediment discharged into the Great Barrier Reef, motivating significant investment to prevent erosion and improve water quality in receiving environments. Large amphitheatre gullies are complex structures with highly variable erosion processes. Process-based models are required to inform rehabilitation practices, and to inform investment at the catchment scale. This project will develop models of gully erosion suitable for informing management in ampitheatre gullies. This project will involve collaboration with Queensland Government and the Queensland Water Modelling Network and is associated with an ARC Industry Fellowship.

Mathematical modelling; Environmental Modelling

MathSciences

Australia's expansive coastal and marine ecosystems are in dire need of improved biological monitoring to preserve their valuable and unique biodiversity in the face of human-related disturbances. This Project responds to the challenge by upscaling and revolutionising fit-for-purpose genetic toolkits that can extract whole ecosystem DNA data from environmental samples. This innovation will be done in the interest of answering previously inaccessible ecological questions related to biodiversity, supporting habitat restoration and engineering solutions, complementing rather than replacing existing biological monitoring, supporting commercial outcomes with automation, and benchmarking the health of coastal and marine ecosystems under threat.

Applied molecular ecology; environmental monitoring

Environmental Science

We are currently looking for a PhD candidate to develop genomic resources and tools for Australian papayas to facilitate future smart breeding of elite varieties. The primary objectives of this role are to: (a) sequence and annotate the reference genomes of selected Australian papaya varieties and (b) develop high-density genetic markers for Australian papaya and wider germplasm collections. The goal is to then uncover genomic sequences that may be used for accurate selection of preferred flavour and productivity traits across a broad germplasm set. Outputs from this project will directly contribute to genomic prediction approaches for developing elite papaya varieties. This project includes molecular, genomic and transcriptomic approaches that will leverage prior knowledge and skills developed in our group and by collaborators. These include high density SNP mapping and QTLs underpinning several fruit quality traits and possible gene candidates, as well as trained sensory panel and biochemical profiling performed to identify volatiles and other compounds that are associated with distinct fruit flavours. Success in this role requires collaboration with fellow team members and leading researchers from the University of Queensland, Murdoch University and the Queensland Department of Agriculture and Fisheries.

Plant Sciene; Crop Genomics; Bioinformatics

BiologicalScience

Microplastics have been widely found in various environments including water, sediment, soil, biota and air. There is growing conern that human can be exposed to microplastics through consumption of microplastic contaminated water and food. This project aim to analyse microplastics in various foods and beverages and assess the human diatary exposure to microplastics and associated health effects

Environmental Science; Ecotoxicology

Environmental Science

This project aims to investigate microplasic contamination of agricultural soils and their fate and impacts on soil and plants as well as potential toxic effects to human via consumption of microplasic contaminated crops

Environmental Science; Ecotoxicology

Environmental Science

In networks with distant parties, light provides an excellent way to transmit quantum information because of its fast propagation and low decoherence. However, these advantages are accompanied by a drawback - the lack of appreciable interactions between photons at the single-photon level, which makes it more challenging to create entangled multi-qubit states with photons compared to other carriers of quantum information. The objective of this theoretical project is to develop optimal state preparation procedures and incorporate them into strategies for showcasing novel quantum cybersecurity protocols.

Quantum Physics

PhysicalSciences

We are offering two Ph.D scholarships for motivated students to work on patterns of species richness and turnover across Australasia, with emphasis on drivers of biotic interchange between Northern Australia and New Guinea. Depending on background and interests, students will have research, travel/fieldwork funds to support work on projects such as (a) diversity and systematics of key groups of frogs or lizards, or (b) broadscale projects on patterns and processes of biotic turnover, quantification of biodiversity hotspots, and implications for conservation.

Ecology and Evolution

EnvironmentalScience

We are developing microfabricated silicon nitride based photonic waveguides to interface with rubidium atoms as a platform for realising quantum devices. The first device in this project aims to demonstrate a wavelength converter from the 780 nm light used in atomic magnetometry to the long-distance telecom compatible 1529 nm light. This is an experimental physics project which includes fibre optics, photonics design work, microfabrication, atomic physics, and vacuum systems with the goal of advancing towards manufacturable devices.

Quantum Physics; Microfabrication

PhysicalSciences

Life is the dynamics of large biomolecules. This project aims to develop a novel experimental approach to achieve atomic levels of control over large biomolecules through manipulation of electrostatically levitated bioparticles in a Paul trap. This starts with single yeast cells and will progress to developing laser and electron optics techniques to controllably fragment the cell into organelles and into isolating single chromosomes. These chromosomes will then be controllably and potentially reversibly unfolded using single electron changes in the static electrical charge to demonstrate an atomically resolve force microscopy. A Gold Coast based joint with the Institute for Glycomics

Biophysics

PhysicalSciences

Trapped ions are a powerful tool for the analysis of charged bioparticles and biomolecules. Paul traps are used for high-resolution, long-duration confinement in this application. However Paul traps have selective stability depending on trap parameters and particle properties. This project would model the impact of permanent and induced electrical dipole moments on the theoretical stability of ion trajectories in a Paul trap and related the limits of electrical confinement of a charge point particle to an electric dipole in an optical tweezers.

Mathematical modelling; Biophysics

PhysicalSciences

Development of new modelling platforms, algorithms and a case study catchment model utilising TUFLOW Catch, a high performance, contemporary practice modelling framework. This will bring higher fidelity, resolution and computational abilities to understanding catchment behaviour for current applications in South East Queensland, but be applicable to any catchments across Queensland and further afield.

Water quality, urbanisation

EarthScience

The project aims to (1) develop an optimised thermo-acoustic refrigerator system (TARS) design, and (2) fabricate a miniaturised TARS with an improved coefficient of performance (COP). The project will determine preliminary TARS parameters based on linear thermoacoustic theory, and numerically optimise critical system parameters, such as the stack geometries, the stack centre position, the resonator length and the compliance volume geometry, in order to improve the COP of an improved and miniaturised TARS using ANSYS computational fluid dynamics (CFD) package.

thermodynamics, computational fluid dynamics, materials science

Engineering

This project will explore application of ultrashort femtosecond (fs) laser pulses to process diamond at a nano- and a micro-scale. In an instance when fs laser pulses are focused into the bulk of a diamond crystal or on its surface, the optical power densities exceeding TW/cm2 trigger non-linear processes including simultaneous absorption of multiple photons, leading to multi-photon ionisation followed by avalanche and impact ionisation. This results in a highly localised distribution of optical, electronic and thermal energy within the diamond lattice. Due to ultrashort duration of fs pulses unlike with longer nanosecond and picosecond pulses, the thermal energy has time to dissipate between consecutive pulses rather than accumulate, minimising processes leading to distortion and defects in the lattice. For that reason the fs laser pulses under right conditions can create very precise 2D and 3D structures in the irradiated materials, including diamond, without much damage to the bulk of the crystal. The project will explore a fabrication of 2D and 3D periodic arrays of closely spaced linear and chiral elements in diamond for near-infrared (N-IR) and IR opto-electronic applications. The PhD candidate is expected to optimise the fs-laser facilities for irradiation of diamond and evaluate the properties and performance of fs-laser irradiated structures and features on diamond using a range of optical and spectroscopic methods. This condensed matter/optical physics project will equip the candidate with solid applied skills in laser processing of materials, nano- and micro-fabrication and materials science and engineering skills.

condensed matter physics, plasma physics, optical physics, materials science

PhysicalSciences

Diamond-like carbon (DLC) is a class of amorphous carbon material that displays some of the typical properties of natural diamond. DLC thin-films have been broadly applied in industries owing to their advantageous properties such as high hardness and environmental inertness. In particular, there is a growing demand for DLC use as protective coatings for cutting tools owing to their exceptional wear resistant characteristics, and there is a strong interest for DLC use in prosthetics and ventricular assist devices (i.e., artificial heart devices) owing to their bio-compatibility. The PhD candidate is expected to optimise the DLC coating vacuum process for deposition of hydrogenated DLC coatings on tool steels and titanium alloys and evaluate the DLC coating properties and performance for selected engineering and medical device applications. This condensed matter/plasma physics project will equip the candidate with solid applied skills in thin-film and nano-fabrication, vacuum technology and materials science and engineering skills.

condensed matter physics, plasma physics, optical physics, materials science

PhysicalSciences

The PhD research will focus on the development of an assessment framework to capture and evaluate the physical, socio-economic and environmental impacts of Australian vertical schools on their surrounding urban environments. It will also measure the capacity to integrate vertical schools in dense urban environments, and the conditioning potential of various policies. PhD candidates with the following background: An interest in urban densification trends and associated infrastructural challenges. Students with interests in urban planning, urban design, active transport, urban policy, social planning, and precinct design could align well with the project. Students who are shortlisted will have an opportunity to work with Dr Matthews to further develop the project aims prior to formally applying for entry to the PhD program.

Urban Policy

BuiltEnvironment

The candidate’s research will focus on social representations of vertical schools. The research will adopt a social representation theory framework and methodology to explore how vertical schools are perceived and represented by various stakeholders. It will also explore how different social representations are translated into vertical school designs and the tensions inherent in these processes. Suitable PhD candidates with the following background: An interest in urban densification trends and associated social theory. Students with interests in urban planning, sociology, urban studies, urban policy, social planning, and cultural theory could align well with the project. Students who are shortlisted will have an opportunity to work with Dr Matthews to further develop the project aims prior to formally applying for entry to the PhD program.

Urban Policy

BuiltEnvironment

Site selection is a critical factor in the deployment of renewable energy farms for electrolysis. Renewable energy farms must be in areas with sufficient wind or solar resources to generate the necessary electricity for electrolysis. Additionally, the availability and quality of water resources must be considered when selecting a site for an electrolysis system. The proximity of the site to existing infrastructure, such as pipelines for hydrogen transportation, also needs to be considered. This project seeks to develop and validate an autonomous smart GIS-based spatial multi-criteria decision support tool using real-world data and scenarios

Spatial energy-water planning

BuiltEnvironment

This project is focused on the detection of asbestos in the residential built environment by using Artificial Intelligence (AI) technologies, including but not limited to deep learning modelling. The aim of the project is to develop a comprehensive understanding of available AI technologies and predictors that can be used for asbestos detection in residential properties. The project will also include evidence-based recommendations in the field of study underpinned by representative case studies. This will enable practical solution for the implementation of Asbestos health risk control policies in Australia and worldwide.

Spatial analytics in the built environment

BuiltEnvironment

Drone, AI and LiDAR technology enables the autonomous identification of materials in buildings. This projects seeks to develop algoriths and tools to autonomously determine the circular economy potential of buildings that will be demolished or rehabilitated. The software tool will facilitate greater understanding on the recyclability and reuseability of building products.

Spatial analytics in the built environment

BuiltEnvironment

The impact of climate change on global wave climate has gain increased attention in the literature recently. The impact of these changes in sediment transport rates is uncertain and may lead to unexpected beach erosion. This is particularly important around obstacles to the longshore transport such as groynes, training walls and headlands as climate change may lead to changes in bypassing rates and their form. This project aims to address these issues on a coastal management point of view by using both data measured in situ and numerical modelling tools.

Geophysics

EarthScience

The impact of climate change on global wave climate has gain increased attention in the literature recently. The impact of these changes in sediment transport rates is uncertain and may lead to unexpected beach erosion. This is particularly important around obstacles to the longshore transport such as inlets as climate change may lead to changes in bypassing rates and their form. This project aims to address these issues on a coastal management point of view by using both data measured in situ and numerical modelling tools.

Geophysics

EarthScience

Waterway pollution, particularly from agriculture or wastewater, can be significant, and adequate monitoring is vital to understand and minimise environmental and ecological damage. Traditional nutrients monitoring requires manual sampling and costly, time consuming lab analysis; however, new optical monitoring technologies, if properly calibrated, allow the collection of much higher frequency, reliable data. Advances in computational capabilities enable machine learning algorithms to identify patterns and extract knowledge from such large datasets. This project will build on existing research work, making use of available sensors and monitoring stations, to collect large amounts of water quality data, and develop predictive models to understand and manage nutrients concentrations in agricultural waterways.

Geophysics

EarthScience

Harmful algal blooms (HABs) are an increasing concern for water utilities and environmental agencies. Their magnitude and frequency has increased in the last years and currently pose a significant risk for water safety and recreational use of waterbodies. Advances in monitoring technology and computational capabilities represent an opportunity to optimise the management of HABs in urban and semi-urban environments. Optical sensors, after specific calibration, allow the quantification in real-time of important proxies for cyanobacteria and total algae, while machine learning techniques enable the extraction of knowledge and identification of patterns from these large datasets. This project is open for a qualified candidate to build on existing research work, encompassing calibration and optimisation of optical sensors employed for HABs monitoring, and development of predictive models to better understand and manage HABs.

Geophysics

EarthScience

The East Australian Current (EAC) transports warm waters from the tropics southward to mid-latitudes. We wish to better understand the role of the EAC in determining water temperature, salinity, and currents in coastal waters, which are used by humpback whales (Megaptera novaeangliae) during migration and breeding seasons. This project will analyse remotely sensed data in concert with field observations and develop numerical models, to investigate the behaviour of and response to the EAC.

Geophysics

EarthScience

This research team is connecting community, business and government to build capacity for accessing local food and to thrive in uncertain times. We work in collaboration with the Queensland Government’s Office of the Inspector General Emergency Management (IGEM) and local governments. Our action-research agenda addresses the industry’s identified needs at this time to build capacity for change among the participants, so they might facilitate change in their respective networks for building local food-related disaster resilience. Community-led decision-making empowers communities to share responsibility for their food resilience in times of need such as severe weather events and pandemic conditions.

Food resilience

Food

Imagine a river system which can alert authorities to unsafe pollution events and identify the time, place, type and quantity of a pollutant that will put at risk its ecosystems. As datasets are becoming larger and more complex, there is an urgent need for the infrastructure sectors to embrace effective training in data intensive scientific methods for effective asset management. This PhD would seek to combine existing sensor networks with big data management and machine learning, to build the next generation of the digital twin, the sentient twin.

Geophysics

EarthScience

Prefabrication aims to improve the efficiency of the construction processes and quality of the constructed facilities that may lead to substantial greenhouse gas emissions reductions over the life cycle of a building project. Nevertheless, the life cycle impacts of prefabrication building projects have not been adequately recognised in the Australian context. The aim of this research is to present a model to assess the embodied carbon emissions which directly address the inherent dynamics of prefabricated residential projects. The model applies the life cycle assessment (LCA) to calculate the greenhouse gas emissions during the processing, building operation, and eventual demolition, dismantling and disposal phases of a prefabricated building project. The model combines Building Information Modelling (BIM) and Discrete Event Simulation (DES) to create the building data specifications of all processes efficiently.

Geophysics

EarthScience

Teaching scholars around the world understand that assessment drives learning (Black & Wiliam, 2009; Boud, 1990; Carless, 2015; Masters, 2014), however there is a lot of variation how academics plan assessment activities (Jessop & Tomas, 2017). Research into best practice for assessment indicates that assessment should be designed to support learning (Baird, Andrich, Hopfenbeck, & Stobart, 2017; Black & Wiliam, 1998; Carless, 2015; Masters, 2014; Weir, 2020). However, the modular approach to assessment within degree planning presents difficulties that students experience in learning from their assessments (Eva et al., 2016; Jessop & Tomas, 2017; Tomas & Jessop, 2019). Academics primarily responsible for learning and teaching need to comply with Federal and institutional policy, and therefore need to ensure the programs they manage or teach into are of a high standard to produce work ready graduates (Medland, 2016; Panigrahi et al., 2015). There are existing gaps in the literature that present practical guidance for academic staff responsible with planning a program of study and policy directives to facilitate this process. Insight into current policies in Australian universities and how these are currently being implemented will provide insights into program level assessment planning and the constraints and enablers to this process.

Education

BuiltEnvironment

This research project aims to create a sensory design framework for the needs of children on the autism spectrum. Due to the sensory abnormalities these children experience, their engagement with objects and environments can be limiting and cause distress to and disrupt their daily lives. The design framework aims to assist and guide design choices when it comes to material, form and colour decisions when creating devices for children on the autism spectrum. There is no current design framework or guide for these children as their sensory needs are so varied. To try and cater to all the anticipated needs, a multitude of aspects will be investigated to describe each material choice

Industrial Design

BuiltEnvironment

Industrial Design

BuiltEnvironment

With the advancement of modern medicine and the aim to control the patient environment to minimise the risk of infection and/or easily facilitate patient care, hospital gardens and the hospital outdoors are an important factor in supporting patient recovery and general wellbeing. The criticality of access to nature for human health and wellbeing, identifies hospital gardens and their health promoting qualities as necessary in new hospital developments. This research will investigate, ‘How can therapeutic hospital gardens be purposefully designed and well-integrated into health policy and hospital programs to optimise their health promoting qualities?’ 

Architecture

BuiltEnvironment

This project covers comprehensive experimental, numerical and theoretical studies of RC flat plate structures in the context of progressive collapse. Three experimental tests were conducted on 1/3-scaled 2×2-bay RC flat plate substructures with 8 or 9 columns. Following the alternative load path (ALP) method, the first test (i.e., 8-column dynamic test) was carried out to investigate the dynamic responses of the substructure during the flexural, punching and post-punching stages by instantaneously removing the interior column and under three incremental levels of gravity loads. The second and third tests (i.e., 9-column static and dynamic tests) were performed, under static and dynamic loading scenarios respectively, on the intact specimens without removing any column. Numerically, a set of high fidelity finite element models was developed to replicate the structural behaviours of tested substructure specimens, and accordingly the Dynamic Increase Factors (DIFs) of both 8- and 9-column specimens were determined. Furthermore, the flexural capacity of the 9-column static test on the intact specimen was predicted theoretically based on the observed yield line patterns.

Structural engineering, progressive collapse, concrete structures

BuiltEnvironment

This project consists of experimental and numerical studies of the strengthening methods for flat plate structures against progressive collapse. The strengthening methods include “hidden beams”, “ring beams”, “stirrups in punching area” and “post-tensioned prestressed concrete slabs”. The focus is post-tensioned prestressed concrete flat plate structures and a series of slab-column joint tests will be conducted. 3D high fidelity finite element models will also be developed and calibrated with the experimental results. Furthermore, a comprehensive parametric study will be conducted to expand the scope of the experimental studies to cover a wider range of design parameters (e.g. slab thickness, level of prestressing forces, arrangement of prestressed tendons).

Structural engineering, progressive collapse, concrete structures

BuiltEnvironment

This study is to develop a new post-tensioned concrete (PTC) flat plate system using high-performance fibre reinforced concrete (HPFRC) for the shear head, which is particularly useful for strengthening the punching prone regions. Post-tensioned prestressing tendons will provide an effective alternate load path in the system, as well as to reduce the amount of steel and concrete usage and the slab thickness. A series of material characterisation and testing programs will be conducted to evaluate the performance of HPFRC with fibers and to determine a desirable balance between the proportion of fibre reinforcement and the concrete strength that could produce desired strength of HPFRC. Subsequently, a series of scaled down slab-column connection specimens will be tested. The strength and the size of shear head will be varied to examine the potential punching shear failure which may occur in different conditions, such as failure happening inside the shear head, outside the shear head, or in both regions. Further, a high-fidelity finite element modelling framework for the proposed PTC system will be developed and validated against the experimental results. Through numerical analysis, the nonlinear behaviour, load transfer mechanisms, and damage modes of slab-column connections under realistic progressive collapse scenarios will be captured. Finally, numerical parametric studies will be performed to study the effects of the strength and size of the shear head, and the slab lateral restraints on the punching shear and post-punching capacities.

Structural engineering, progressive collapse, concrete structures

BuiltEnvironment

One of the many challenges of managing cable-stayed bridges is the development of numerical models that can accurately assess dynamic characteristics, such as modal frequencies, mode shapes, and modal damping values of these highly redundant systems. Ongoing developments in sensor and data acquisition technologies have made it possible to install extensive structural health monitoring (SHM) systems on many new and existing bridges which affords the opportunity to validate and update models of these structures. While most bridge-SHM studies published focus on finite element (FE) model validation, very few use updated models to further investigate the condition of critical structural components. Furthermore, the challenges of considering the inherent uncertainties in model updating of long-span bridges are often ignored. Using an in-service case study bridge fitted with an SHM system, this research project proposes to create and update an initial FE model of a cable-stayed bridge, and use this model to evaluate the condition of the stay cables through natural frequencies and cable tension as part of the bridge’s overall SHM efforts. A Bayesian approach to model updating will be considered to account for uncertainties.

Structural engineering, structural health monitoring, bridges

BuiltEnvironment

Implementing offshore hybrid renewable energy systems have strong potential to accelerate our transition to a net-zero economy. However, there is a lack of a systemic and autonomous spatial approach to evaluate the potential sites for coupled wind and solar, considering multi-criteria covering environmental, technical, economic and social aspects. The main objectives of this study are: (1) To determine the energy mix scenario for offshore wind farm and solar power plants considering the energy demand in Australia; (2) To develop a tool to autonomously identify optimal site locations for offshore wind and solar plants considering both environmental and social constraints/impacts as well as energy demand; and (3) To develop an interactive spatial heat map of potential optimal locations. The Multi Criteria Decision Making (MCDM) approach will be applied to consider economic, technical, social, and environmental aspects. MCDM methods have become popular for the assessment of renewable energy resources due to consideration of preferences of decision makers and various conflicting criteria. Moreover, the project will create a spatial data platform that autonomously collects and dynamically analyses various datasets using statistical and MCDM methods. The platform will include a user-friendly GIS-based visualisation module that displays suitable locations for offshore renewable energy systems to decision-makers. The developed tool will be validated for the Australian coastal context but will be designed so that it is generalisable worldwide in order that is has far reaching application.

Coastal Engineering, Marine Renewable Energy, Environmental Fluid Dynamics

Engineering

The world-first, highly interdisciplinary SeaCities Lab creates solutions to turn coastal risks into opportunities, such as amphibious and aquatic floating structures. We are lateral thinkers and experts, developing innovative design solutions with projects ranging from research through to implementation. Our PhD researchers are an important part of the SeaCities research group which is located in own premises at Griffith's Gold Coast Campus. We are involved in research projects with the government, industry, and community-based organisations. SeaCities is supported by the Cities Research Institute and connected to an international world-class network. Further Information: Inventive and passionate PhD applicants are welcome to contact us via email including a CV and a portfolio of previous works (pdf with max. 20MB).

Interdisciplinary research between design, science, environment etc.

BuiltEnvironment

In this project, the classical 5-stage life cycle assessment (LCA) of a variety of common by-products or wastes suitable for construction use shall be carried out. Mining by-products (e.g. fly-ash, slag and red mud) and landfill wastes (e.g. plastic, construction and demolition wastes) are considered recyclable or reusable materials due to their inert but ‘attractive’ physico-chemical properties that would require deliberate and specific ‘activation’ processes. Combining the LCA framework with the powerful big data analytics driven by artificial intelligence Bayesian Inference method, a trainable, updatable and reliable predictive model can be developed to optimise the life cycle of each of the reclaimable waste when it is used in a typical construction project. For example, fly-ash is a by-product of coal combustion and once it is chemically-activated (called geopolymer or ‘green cement’), it has superior mechanical properties than the environment-polluting cement.

Geomaterial science, geomechanics, machine learning

Engineering

In this project, the physical and strength attributes of aged and new, but recycled rail track ballast are assessed via high-end laboratory testings. Critical strength parameters are then interpreted via interlocking and shearing behaviour of granular ballast interaction. Supplementary field instrumentation using continuous fibre optic sensing method running parallel to the rail tracks and rail sleepers in order to evaluate the complex 3D recycled ballast – structure responses. This is carried out to benchmark the accuracy of Discrete Element Modelling (DEM) method so that DEM could then be used as a sustainable and reliable modelling method to interpret ballast-structure interaction using lab-derived interlocking strength parameters.

Geomaterial science, field instrumentation, discrete element modelling

Engineering

Pipe jacking technology is becoming more popular in urbanised areas because it is environmentally friendly and cost-effective. This technology is an excellent alternative to open-trench methods for pipeline construction. To explore the soil-pile interface behaviour in laboratory tests, the Direct Interface Shear test machine is modified to receive a split-window loading ram on a fully-automated loading and shearing system and a purpose-built transparent shear box. This is then followed by using actual concrete surfaces to mimic the surface finish of different types of pile foundations (e.g. pre-cast vs cast in-situ). Based on Particle Image Velocimetry (PIV) of discrete element method (DEM) technology applied to the Direct Interface Shear test adopting the transparent shear box, the shear failure (sliding) mode at the pile-soil interface can be clearly distinguished through the reliable digital visualisation technique, thus tracking the movements of granular particulate behaviour from start to finish so as develop greater understanding on the corresponding plane-stress, stress-strain relationship.

Geomechanics, discrete element modelling, interface shear test

Engineering

The study of the strength of reconstituted and stabilised soft soils such as dredged mud off Brisbane River is very important in geotechnical engineering. The soil particles such as clay, sand, and silt play important roles in determining the behaviour of soils. The behaviour of clay and sand particles are unique; however, the behaviour of silt particles lie in a transitional form between sand and clay. Therefore, this project seeks to investigate a) the effect of silt contents on the strength of soft soils; b) the effect of silt content on the strength of cement-stabilised soft soils; and c) the microstructure and performance of the soft soil specimens stabilised by cement and/or additives as a form of sustainable ground improvement methods.

Geomaterial science, ground improvement, microstructural analysis

Engineering

This PhD project falls under the broader iKnow, weKnow research initiative funded through an Australian Research Council (ARC) Linkage grant and supports industry-research partnerships to address industry research problems. The name iKnow weKnow represents the combination of Indigenous knowledge, technical water and energy knowledge and digital technologies, together with the concept of inclusion and moving from a focus on individual behaviours to collective, two-way action to address water and energy security in a climate changed future. The PhD will focus on identifying and piloting best practise approaches for encompassing Indigenous Environmental Knowledge and Western Science into essential service management discrete First Nations communities. The PhD research will have elements of action research and draw on mixed methods and will suit candidates interested in contributing to impactful research outcomes and building skills in interdisciplinary and transdisciplinary research.

First Nations community engagement, water and energy management

EnvironmentalScience

The PhD project will explore pragmatic research questions relevant to the project aims, and may include Indigenous governance, Indigenous pedagogies and Indigenous methods of community engagement, co-design processes and integration of multiple types of knowledge – e.g. traditional, local and Indigenous Ecological Knowledge, with technology and digital interfaces to identify effectiveness of uptake and impacts on sustainability of supply and end uses of energy and water in remote community contexts.

First Nations community engagement, water and energy management, co-design of digtial and community tools, water and energy management

EnvironmentalScience

Our study proposes a hybrid model using computer simulations of submerged breakwaters to generate data for a data-driven system that optimizes the structural design given the coastal environmental conditions. Due to the complex interaction of environmental and design variables, the study of submerged breakwaters are expensive and time-consuming. While there are multitudes of benefits, such as surfing and diving amenity, the influence of submerged breakwaters on the shoreline must be understood to implement a project. The proposed model would allow for a rapid assessment of data-driven solutions backed by process-based models that are used by engineering consultancies for coastal modeling.

Numerical and data-driven modeling, Engineering, Environmental Science

ComputationalScience

To provide faster and more accurate solutions for complex problems in renewable energy-integrated networks, this project will develop new artificial intelligence- based approaches for these nonlinear systems. This project will lead to a significantly reduced carbon footprint in energy systems and help achieve Australia's 100% renewable energy target by 2025, which is the world's leading target.

ElectricalElectronicEngineering

Engineering

Renewable energy-integrated systems are very complex to analyse and control since more uncertainty is introduced into the energy system operation, which pushes them to their operational limits and more nonlinear and complex behaviour. This makes the security assessment of the energy system a central tool to predict and mitigate risks for partial or total blackouts. Due to the size of the problem of assessing energy system security, the computational complexity makes it impossible to evaluate all possible operating points for systems with more than a few tens of nodes. The goal of this project is to propose an algorithm that improves the accuracy of prediction of energy system security through AI-based simulations, which leads to more resilient electric power networks.

ElectricalElectronicEngineering

Engineering

Counting flowers is a commonly used method to predict future fruit yield. This project will entail the development of a system and method to enable automated flower density mapping that can be used to estimate the future yields in each field for better harvest planning. The provisional implementation will be a tractor mounted camera that will count the flowers as the tractor is spraying the fields. However, drone mounted cameras may also be considered.

Machine learning, AI, Computer vision, Mobile Communications

EnvironmentalScience

This project aims to investigate and develop an automatic segmentation algorithm that determines the start and stop times of swallowing events from audio recordings of babies and pre-term neonates. Currently, swallowing events are manually segmented by clinicians, so it would be of great benefit for a computer to automatically do the segmentation. Once segmented, the next task is to investigate machine learning algorithms to classify whether these swallows are normal or aspirating. Knowledge of theory and implementation in signal processing, speech processing, machine learning, deep learning methods, and Python programming is preferable.

signal processing, machine learning, artificial intelligence

Engineering

When fruit is packed by human packers they tend to pack excess fruit to speed the packing process which means that farmers suffer significant losses through this “give-away” of fruit. During the fruit packing process, there is a standard punnet weight, packers are expected to pack to this exact weight per say. However, in practice, excessive fruit is frequently packed, especially by inexperienced packers, which incurs significant loss to farmers in the form of “give-away” fruit. The project will investigate how packers pack fruit and in particular it will investigate how a machine learning model that monitors packer’s packing behaviour and adjusts the target pack weight can be used to reduce fruit “give-away” thus reducing loss to farmers. Another point of investigation is the aesthetic beauty of packing, so the punnet appears to be attractive to customers, this requires the packer to have jigsaw puzzle solving skills for packing eye-pleasing punnets with fruits of various sizes distributed symmetrically. The project will also investigate ways of eye-pleasing packing patterns and propose attractive fruit distribution schemes that promote product sales.

Machine learning, AI Geometric Aesthetic

EnvironmentalScience

Plant diseases (e.g., crop and cotton) are a major threat to food security and industrial production, but their rapid identification remains difficult in many parts of the world due to the lack of the necessary infrastructure. Furthermore, there exist different types of plant diseases, it is necessary to classify them effectively. The problem of detecting and classifying image-based plant disease has attracted a lot of attention from researchers. However, there are still many challenges in accurately and efficiently identifying and classifying plant diseases, e.g., how to effectively identify and classify plant diseases under extremely limited sample conditions. This PhD project aim to develop novel deep learning techniques for solving the problem.

Machine learning, AI Geometric Aesthetic

EnvironmentalScience

The primary goal of this research project is to develop photoactive nanomaterials, in particular nanocarbons and hierarchically-ordered metamaterials, with the tenet of green chemistry. We explore their applications in environmental monitoring and remediation, renewable energy and health care.

Micro- and Nanotechnology

Technology

Borders are ubiquitous and multifaceted constructs of contemporary existence that are increasingly used as devices of separation and control. This research seeks to subvert the dividing connotations of the border via the architectural process of Re-Bordering, a newfound theory whereby existing borders are reconstructed into zones of interaction and exchange. Re-Bordering stems from the theoretical distinctions between the border and boundary established by Stephen Gould (Ecologist) and Richard Sennett (Sociologist). In this study, the application of Re-Bordering focuses on softening the border/s between host and refugee communities. Re-Bordering is, therefore, positioned as an apparatus for interaction, connection and exchange between refugee and host communities. Re-Bordering aims to establish a sense of belonging, mitigate against discriminatory attitudes and widen social networks for refugee populations. The promise of increased community connectedness facilitated through Re-Bordering benefits the host community by promoting more permeable, adaptive, and resilient communities. The study adopts a Research by Practice methodology that is central to developing, testing, and understanding the concept of Re-Bordering from an architectural perspective.

Architecture

BuiltEnvironment

This project aims at establishing key technologies for the integration on wearable device. The first technology is flexible electronics and sensors such as electrodes and field effect transistors for bio sensing. The second technology is fluid handling on a flexible substrate that includes sample collection, transport and mixing. The thrid technology is near-field wireless communication that can transfer data beween the wearable/implantable device and common devices such as smart phone and smart watch. The outcome will be a suite of technologies that can be integrated on a smart patch wearble on skin or implantable in the body.

Electronics Engineering, Mechanical Engineering, Micro- and Nanotechnology

Engineering

The project aims at developing enabling technology platforms that utilise flexibility and stretchability for handling liquids. The three major focuses are cor/shell beads as a digital microfluidic platform, stretchable microfluidics for enhanced mixing and separation, and viscoelastic fluid flow for enhanced mixing and separation. These activities are currently supported by a team funded by an ARC DP project. The outcomes will have direct impact in the development of wearable diagnostoc devices such as smart skin patch for monitoring sweat.

Fluid Physics, Micro- and Nanotechnology, Mechanical Engineering

Technology

This project continues the activities of a recently completed PhD project. The technology utilises the strecthabilities of device material to fine tune device features in the sub mincrometer scale. The technology allows for precise control of spacial dimnesions that matches the size of biological particles such as cells, viruses or extracellular particles such as exosomes. The project aims at the three major applications for handling of biological samples in microscale: mixing, trapping and separation. The outcome will have a great translational potential, particulalry for the sector of biomedical instruments.

Fluid physics, Micro- and Nanotechnology

Technology

This project explores applications of silicon carbide (SiC) on silicon (Si) platform technology. We are particularly interested in the energy efficiency of new devices. The application of the hybrid materials is legend and includes the areas such as: semi-conductor memories; power-integrated electronics; micro electromechanical systems (MEMS); growth platform for other compound materials such as GaN and as a heterojunction material for solar cell and high-gain bipolar devices. To date, the main application has been the use of 3C SiC onto Si for memory applications and this will continue to be one of our main drivers.

Micro- and Nanotechnology

Technology

This project covers three key themes, including: sustainable energy technologies; novel devices and materials theory and modelling. As part of this project, our members are involved in studies that will lead to advances in the health, energy and mining industries. Such studies include: research on the design of materials for clean energy applications; commercially oriented research on design of surfaces and interfaces for memory applications; applied research on the development surfaces for environmentally friendly mineral flotation and gas storage and theoretical and computational research on the passage of fluids through pores and the interaction between surfaces.

Micro- and Nanotechnology

Technology

Investigating efficient conversion of electrical energy beyond the limits of silicon-based technology. Increasing demand for efficient conversion of electrical energy has motivated research and development beyond the limits of the well-established silicon-based technology. The dominant control switch in electronics is the metal–oxide–semiconductor field-effect transistor (MOSFET), which complements diodes in power-electronics systems. Silicon carbide (SiC) MOSFETs enable improved power efficiency and increased switching frequency, which in combination with SiC Schottky diodes reduces the size of power systems. Both SiC Schottky diodes and MOSFETs can operate at much higher temperatures than silicon devices, which enables applications not possible with silicon power systems. This is especially important in applications for solar inverters and car electronics.

Micro- and Nanotechnology

Technology

This project investigates the wide energy gap of gallium nitride, which enables the emission of the highly energetic blue light. The wide energy gap of gallium nitride (GaN), which enables the emission of the highly energetic blue light by light-emitting diodes, is very attractive for the development of power devices. An additional advantage for these devices is the inherently high mobility of current carriers. These devices are referred to as high-electron mobility transistors (HEMTs).

Micro- and Nanotechnology

Technology

Alerts about forest fires shouldn’t depend on pets smelling smoke, instead, we need smart infrastructure which has zero-power sensors, quick response to fire or smoke and long-distance communication. In this project, event-driven sensor systems will be developed for early detection of bushfire outbreak in Australia. The detailed tasks include the development of event-driven sensing elements, wake-up strategy, no-solar energy harvester, electronic circuits, long-range radio frequency transmissions (e.g. LoRaWAN), etc.

Sensor network, electronic engineering, internet of things

Engineering

Wearables are mobile electronic devices that can be embedded in clothing or as an accessory to monitor the behaviour of human activities or living organisms. It can also be used for employee monitoring and to improve safety in the work environment. Not only can they provide reassurance and comfort to many workers, but they could change a potentially high-risk environment into a safe one. There are now numerous emerging flexible and wearable sensing technologies that can perform a myriad of physical, chemical, and physiological measurements. Rapid advances in developing and implementing such sensors in the last several years have demonstrated the growing significance and potential usefulness of this unique class of sensing platforms. The key issues in the design of wearable sensors include their sensing accuracy, conformability, size, lightweight, stretchability, low cost, and power consumption. However, polymer-based sensor development is a promising area to address these issues. The phd project will experimentally and numerically understand and exploit the sensing properties of Polymers; and develop a novel micromachined sensor with polymeric sensing layers for wearable application.

Micro and Nanotechnology, material science

ChemicalSciences

In this project, we will develop an energy harvester, which can harvest vibrational energy from environment to electrical energy and provide power for no-battery sensors. For example, in wind turbine, the health monitoring sensors of propeller need continuous power supplies: the traditional batteries are not suitable and need replacement periodically. Other examples include remote area sensor networks, wild animal behaviour monitoring, bush fire early alarm networks, etc. A miniaturized and high-efficiency energy harvesting device is needed to be developed in this project. The device can convert kinetic vibrational energy to electrical energy.

Electrical and Electronic Engineering, mechanical engineering, micro and nanotechnology

Engineering

In this project, capacitively coupled electrocardiography (dry ECG) health monitoring systems will be developed. Compared to traditional resistive lead connection ECG (wet ECG), the proposed dry ECG does not require direct contact to skin. Therefore, it has the benefits of no skin irritation, long-term comfortable use and mobility, patient self-monitoring, etc. The detailed tasks include the development of capacitive electrodes, low-noise ECG signal amplification circuits, wireless communications to smart phones, preliminary ECG tests, etc. Skills include electronic circuit design and PCB implementations, small analogue signal amplification, wireless data transmission via Wi-Fi or Bluetooth, microcontroller programming, etc.

Wearable sensors, electronics

Engineering

This project is to develop a high frequency power transformer integrated with SiC MOSFET for Smart Power Router and Solid State Transformer used in Microgrids and electric vehicle (EV) charging system. The student is required to have an experience of power electronics and electric machines, and the FEM based simulation tools or knowledge of computational electromagnetics.

Electrical and Electronic Engineering

Engineering

This project is to develop an Energy Management System (EMS) for the advanced AC/DC hybrid Microgrids with Energy Storage Systems (including flywheel energy storage with high frequency rotary transformer and separately excited synchronous machine or wound-rotor synchronous machine), renewable energy systems and Ultra-Fast EV charging stations. The student is required to have an experience of Microgrid and its control system, and knowledge of power electronics and electric machines.

Electrical and Electronic Engineering

Engineering

The cost of rare earth permanent magnet along with the associated supply volatility have intensified the interests for machine topologies, which eliminate or reduce the rare earth magnets usage. This project will develop a High Frequency Rotary Transformer (HFRT) for a separately excited synchronous machine (SESM) or wound-rotor synchronous machine (WRSM) with brushless and no permanent magnet for power generators and electric motors. These electric machines will be used in offshore wind turbine and motor/generator unit in flywheel energy storage, and brushless WRSM in EV Motors will be introduced to BMW and RENAULT as they are still using conventional WRSM with brush and slip-ring in their EV motors currently). The students are required to have an experience of using the FEM based simulation tools and computational electromagnetics background.

Electrical and Electronic Engineering

Engineering

Beach-dune systems can act as natural barriers to protect coastal regions from inundation by storm surge and wave action and are also important ecosystems providing natural habitats for various animals and plants. Sandy beach-dune systems evolve under the influence of a complex combination of hydro- and morpho-dynamics coastal processes across a wide range of spatio-temporal scales. Over decadal timescales the effect of longshore sediment transport (LST) processes on coastal evolution is evident. However, hindcasting or projecting future patterns of LST patterns, itself, is really challenging due to the uncertainties arising from different sources. Due to climate change (CC) impacts on atmospheric circulation, global and regional wave climates of many coastal regions around the world might change. As the wave parameters control the wave energy flux; any changes in these parameters could result in significant changes in patterns of coastal sediment transport and resulting coastal evolution. It should be noted that the effect of changes in the LST patterns on coastal evolution can be comparable to or greater than the impact of sea level rise (SLR). Although the impacts of global CC specifically on LST patterns, have been studied for some cases around the world, the reliability of their projections is questionable as they poorly addressed uncertainty evolution in the projections. Sampling uncertainties, at least from the main sources including emission scenarios, wave forcings, and sediment transport models needs to be considered. In this way, the level of confidence and robustness of the projections can be determined. Moreover, finding the contribution of each source of uncertainty to total uncertainty is a vital step towards reducing uncertainty ranges in the projections. In light of the above, the present research work aims to study the climate change impacts on wave driven LST patterns, and to provide insight on uncertainty evolution in the LST projections. For this purpose, first, a comprehensive literature review was conducted to identify the gaps, and to clearly define research aims. Then, three phases (one for hindcasting and two for projecting LST patterns) were considered. Seven sites along a non-straight coastline (Gold Coast, Australia as the case study) were selected to conduct the required experiments for each phase of this research.

Coastal engineering, Marine Renewable Energy, Environmental Fluid Dynamics

Engineering

The PhD research will focus on the development of an assessment framework to capture and evaluate the physical, socio-economic and environmental impacts of Australian vertical schools on their surrounding urban environments. It will also measure the capacity to integrate vertical schools in dense urban environments, and the conditioning potential of various policies. Students with interests in urban planning, urban design, active transport, urban policy, social planning, and precinct design could align well with the project. Students who are shortlisted will have an opportunity to work with Dr Matthews to further develop the project aims prior to formally applying for entry to the PhD program.

urban planning, urban design, active transport, urban policy, social planning

BuiltEnvironment

The candidate’s research will focus on social representations of vertical schools. The research will adopt a social representation theory framework and methodology to explore how vertical schools are perceived and represented by various stakeholders. It will also explore how different social representations are translated into vertical school designs and the tensions inherent in these processes. Students with interests in urban planning, sociology, urban studies, urban policy, social planning, and cultural theory could align well with the project. Students who are shortlisted will have an opportunity to work with Dr Matthews to further develop the project aims prior to formally applying for entry to the PhD program.

urban planning, sociology, urban studies, urban policy, social planning, cultural theory

BuiltEnvironment

Wireless sensor network offers a more cost-effective, accurate and faster Internet of Things (IoTs) solution compared to traditional technologies, which require excessive labour and maintenance costs. This project will develop a wireless sensor network for smart cities utilizing the low-power long-range wide-area network (LoRaWAN) technology. Unlike wired sensor network, it can significantly reduce energy consumption and lower the development and maintenance costs. The raw real-time data will be processed locally at the sensor node and the meaningful data will be transferred wirelessly to the gateway more than 3 km away. The long distance can dramatically reduce the quantity needed for the sensor nodes, thereby covering much larger areas to be monitored. The gateway can then send the data to the Cloud for analysis and forecasting to build an early warning system based on artificial intelligence. The possible applications include water and air quality monitoring, as well as structural health monitoring for building, bridge, power pole, wind turbine, etc.

Smart sensors, Sensor network, Internet of Things, artificial intelligence

Engineering

Due to its remarkable properties, such as resistance to elevated temperature, radiation, and chemically harsh environments, Silicon carbide (SiC) is emerging as an ideal candidate for microelectromechanical system (MEMS) devices operating in extreme conditions. This project aims to develop a novel, robust, reliable, and heavy-duty sensing platform of Silicon Carbide on high temperature substrates, that can be used to manufacture low-cost and highly sensitive miniaturised sensors for harsh environments. The detailed tasks include investigation of mechanisms of anodic bonding and oxidation of 3C-SiC thin film, as well as development and demonstration of pressure sensors/transistors under high temperature and corrosive conditions.

Advance manufacturing, sensor, Micro and Nanotechnology, semiconductor.

PhysicalSciences

High risk industries reliance on procedures is high; there are checklists, memory items, procedures, manuals and rules that direct how a cockpit should be configured, what to do in an emergency and whether an aircraft can take off given the physical environmental conditions. Despite their relevance, the number of procedures and rules is increasing every year without a direct translation into a reduction in the number of accidents and incidents. As an alternative to the current approach to procedures, which are seen as the only way to create safety, resources for action see procedures as a supplement to the activity. It provides the information required to complete a task if and when the worker needs it. However, how do procedures as resources for action look like in practice? In this research project, we aim to develop normal and abnormal situations checklists sensible to the context that provide the information needed, when needed, if needed.

HCI

Technology

This study focuses on leveraging machine learning techniques to analyze and visualize birdstrike reports, aiming to improve aviation wildlife management strategies. By processing extensive birdstrike data, the research seeks to identify patterns, trends, and risk factors that contribute to wildlife-related incidents. The machine learning model will generate interactive visualizations that provide stakeholders with a comprehensive understanding of birdstrike occurrences, enabling more targeted and effective management efforts. Ultimately, this study aims to reduce birdstrike incidents and enhance aviation safety while promoting wildlife conservation by providing data-driven insights to inform decision-making in wildlife management initiatives.

ornithology, conservation, airport safety, aviation sustainablity, machine learning, data mining.

EnvironmentalScience

Hydrogen is becoming popular as a sustainable source of energy globally. The Australian government is currently funding millions of dollars in multiple projects to promote usage of Hydrogen. According to the Low Emissions Technology Statement of the Department of Industry, Science and Resources (DISR), Australia aims to reduce costs and develop innovative technologies to meet the government’s goal of hydrogen production under $2/kg. Current pipeline infrastructure, which is mainly designed for transporting natural gas, has several limitations for hydrogen transport, such as: embrittlement and cracking of steel pipelines, performance evaluation and performance testing. Reinforced thermoplastic pipe (RTP) can be a solution due to its high chemical resistance, durability, flexibility and cost-effectiveness. The aim of this study is to provide recommendations for design of RTPs with variable fibre thicknesses and fibre orientations under different internal pressures. The proposed RTP consists of an inner high-density polyethylene (HDPE) pipe reinforced by glass fibre wrapping (GFRP), and then inserted into an outer HDPE pipe.

Sutainable energy

Engineering

This project aims to explore the structural and vortex-induced-vibration performances of novel deep subsea pipe-in-pipe designs, using a textured outer pipe. Deep-water pipelines and risers are vulnerable to structural damages due to high hydrostatic pressures, large thermal loads from the internal hydrocarbons, and adverse vibrations in the currents. The expected outcome of this project is an experimentally validated pipe-in-pipe system that can resist higher pressures, is less sensitive to dents or imperfections, and has suppressed vibrations. The research results will find direct applications in the designing of pipelines, and will provide significant benefits in terms of increased reliability and safety for the oil and gas industry.

Offshore Energy

Engineering

Safety management systems are a reality and a requirement in many industries, from aviation and healthcare, to oil and gas and constructions. Also known as Occupational Health and Safety (OHS) System, Health, Safety and Enviroment (HSE) Systems, these systems have not been able to improve the safety records as expected and the limitation pointed out by many scholars is the reliance on outdated assumptions and limited evidence. Considering new approaches to safety management, such as safety-II, safety differently, resilience engineering, and other, this research project aims to analyse the limitations of safety management practices commonly employed by safety management sytsems and update or develop new practices. The ultimate goal is to help industry make their SMS more effective.

OHS

Management

Simulators and training devices are applied in a range of educational settings. From vocational and tertiary degree to high risk industries, these educational technologies are engaging, they place students at the centre of the learning process, force students to be active and serve as a great risk-free enviroment for safety critical training. Despite being extensively used for trianing operators in aviation, maritime and rail, there is still a perception that high fidelity simulators are always a preferred technology. The assumtion is the similar to a real enviroment, the better. However, recent research has shown that simulators and training devices will never fully reproduce reality and the low fidelity ones are as helpful if employed with appropriate pedagogy and support materials. In this research project, the objective is to assess how different simulator and training device tehnologies can employed to enhance training. Which tasks and skills can be developed accross a range of devices taking into consideration learning objectives, training outcome, quality, length and costs?

Transport

Technology

A window wall is a lightweight external wall composed of glass and aluminium, which does not carry any gravity loads from the building, except for its own weight. The wind load applied to this façade system is transferred to the main structural system through the connections of sub-frames (consisting of sub-heads and sub-sills) with slabs. The aluminium sub-head flange due to its long length is susceptible to bearing failure under this loading condition, a phenomenon that has not been adequately researched yet. This PhD project investigates the bearing behaviour and design of aluminium sub-heads through comprehensive experimental testing and advanced numerical modelling.

Structural Engineering, Facade Engineering, Aluminium Structures

Engineering

Light gauge steel has played a vital role in construction industries in recent years. This project will investigate the structural behaviour and design of cold-formed steel frame walls in modular buildings. A series of experimental study will be performed in the lab to evaluate the strength and performance of various steel frames under wind pressures. Advanced finite element models will be developed, and the validated FE models will then be used to undertake a detailed parametric study, including a wide range of cold-formed steel members and linings. The current design guidelines provided by Australian, American and European Standards will be assessed and new design rules will be proposed as required.

Structual Engineering, Modular buildings, Steel Structures

Engineering

Sustainable water supply management in regional areas in adopting climate change

Sustainable water supply management

Engineering

Construction supply chain management

Engineering

This study aims to utilize advanced machine learning techniques to assess the effectiveness of aviation wildlife management programs in mitigating the risks associated with wildlife-aircraft collisions. By analyzing historical data on wildlife strikes, bird populations, and implemented management strategies, the study seeks to identify patterns and correlations that can inform the optimization of future wildlife management efforts. The machine learning model developed in this research will enable stakeholders to make data-driven decisions, enhancing the safety and sustainability of the aviation industry while simultaneously promoting wildlife conservation.

sustainability, biodiversity, wildlife monitoring, aviation safety, machine learning, A.I., data mining

EnvironmentalScience

This study focuses on the innovative application of weather radar data to locate geo-spatial hotspots of bird populations in the airspace. By examining high-resolution radar imagery, researchers will analyze bird density, movement patterns, and the influence of environmental factors on bird behavior. The study aims to provide critical insights into the spatial distribution of avian hotspots, facilitating the development of more targeted and effective aviation wildlife management strategies. Furthermore, these findings can contribute to the understanding of bird migration patterns, promoting the conservation of avian species and improving airspace safety.

sustainability, biodiversity, wildlife monitoring, aviation safety, machine learning, A.I., data mining

Aviation

The use of electrokinetic (EK) treatment which is a methodology being investigated in some parts of the world as a viable in situ soil remediation and treatment method. The principles of EK treatment method involve applying a low direct current or a low potential gradient to electrodes inserted in the low permeable soils that cannot readily drained. The transportation of charged fluid across the soil involves several complex mechanisms such as electrolysis, electro-osmosis, electro-migration and electrophoresis. This technique can also be applied/enhanced by introducing desirable non-toxic chemical compounds.

Ground improvment

Engineering

Soil stabilisation is an in-situ soil treatment in which soils are mixed with cementitious or other chemical stabilising agents. Determining the unconfined compressive strength (UCS) of stabilised soil is a principal task in the design and construction of the ground improvement. Hence, this study aims to develop a reliable predictive model for the UCS of clay stabilisation with common cementitious binders using the gene-expression programming (GEP) technique.

Ground improvment

Engineering

The friction of runway pavement is critical for the safety of aircraft landing and movement on the runway. Tire hydroplaning may lead the aircraft to move off the runway and hinder the safe landing during wet weather conditions. Grooving on the runway is one way to develop frictional braking resistance and diminish hydroplaning's potential risk by improving runway surface drainage capacity during damp weather. According to the Federal Aviation Administration (FAA), groove construction must follow specific dimensions to maintain skid-resistant airport pavement surfaces. However, the groove area can be reduced for several reasons, and regrooving is essential if 40% of the runway groove of a substantial length decreased to 50% of its original dimension. Grooves initiate different potential distress mechanisms that are not found in an ungrooved pavement surface.

Pavement

Engineering

Airport pavements are widely constructed as airport runways, taxiways, and aprons. The airport traffic should be considered during the design stage of airport pavements before the construction. To protect airport pavements from negligent overload, a pavement strength rating system and an aircraft load classification system are adopted. Under the rating systems developed by the International Civil Aviation Organization (ICAO), Aircraft Classification Number (ACN) and Pavement Classification Number (PCN) are calculated to assess the aircraft loads and the load-carrying capacity of the airport pavements for unrestricted operations. With the increase in the air transport demand, increasing attention on the continuous evaluation of airport pavement conditions is introduced to airports across the globe.

Pavement

Engineering

Geopolymer, also termed as alkali-activated material (AAM), is a promising concrete technology having the advantages of low carbon emission and resource reclamation. Intensive studies have verified the feasibility of using industrial waste (IW) as an aluminosilicate precursor (AP). However, the inconsistent characteristics of IW are an obstacle against its industry scale application. Moreover, most mix design methods are limited to a particular type of local material, which fails to consider disparities and is unable to maintain stable AAM performance. Hence, this project is to evaluate of IW, reactivity enhancement, and target performance mix design method.

Construction Materials

Engineering

This study examines the relationship between the compression and tension characteristics of geopolymer mortar (GM) made from low-calcium Class F fly ash with two types of fine aggregate—sand and waste glass cullet—under ambient and oven curing methods. This study also requires the comprehensive carbon footprint estimates for both geopolymer and ordinary Portland cement (OPC) concrete, including the energy-expending activities associated with the transport of raw materials, manufacturing, and concrete construction.

Construction Materials, carbon footprint, Waste

Engineering

With the development of 3D printing concrete technology, geopolymers are considered to be the substitutes for traditional concrete materials as 3D printing concrete materials. This project aims to consider how the mixing proportion, additives, printing configuration and the post-conservation conditions influence the mechanical properties, hardness properties, fluidity, sustainability, durability and printing accuracy of 3D printing geopolymers, so as to select the most suitable 3D printing geopolymers for the construction of large-scale buildings, reduce the construction costs, labor costs and construction waste, improve construction efficiency, and achieve the purpose of environmentally friendly construction.

Construction Materials, carbon footprint, Waste, 3D printer

With the increasing popularity of tall timber buildings, projects have been funded to understand the behaviour of individual components (such as connections) and develop new hybrid floor systems. All projects are linked to industry and aimed at changing the way timber buildings are designed and built.

Construction materials, timber, steel, concrete, experimental and numerical analyses

Engineering

The Commonwealth has forecast Australia’s urban freight volumes to grow by over 50 per cent from 2020 – 2040, with increases in both urban and inter-urban road freight. Truck fleets will be converted to emissions-free and more automated driving technologies, dramatically improving sustainability, safety and amenity. But with limited capacity to increase road space, making more efficient use of existing arterial networks is essential for Australia’s cities and ports. New datasets are being developed such as the Prototype National Freight Data Hub; machine learning techniques and video data feeds provide other opportunities to collect freight data. This project works with transport modellers and experts in urban road infrastructure to help create and test advanced methods and new models for road freight planning, and to use these to evaluate different ways to try and meet, or better manage, urban travel demand. Suitable PhD candidates with the following background: a disciplinary background such as transport engineering, planning, economics or urban spatial analysis, with a preference for candidates with skills in Python coding, R software, geographic information systems, machine learning, video data analysis, and/or 4-step modelling software. Research Environment: Griffith’s transport research team is highly ranked internationally, has an enviable peer-support program involving staff and PhD students, and is housed in one of Griffith’s premier buildings (the Sir Samuel Griffith Centre). This PhD will be supported by our funding agreement with the Queensland Government, including with additional conference funding support, and will likely involve a work placement with experts in a major road industry partner

Transport planning, transport modelling, freight

BuiltEnvironment

Australia’s national highway system, and especially its urban arterial roads and connections to urban ports, are often struggling to maintain reliable access for freight movement, despite the higher-value of freight movements to the economy. Truck lanes on arterials, freight bypasses, and dedicated truckways to key industrial and port zones, can increase the efficiency and sustainability of the transport system, and reduce truck movements on inappropriate routes through sensitive urban neighbourhoods. Truck lanes can also improve non-freight operations, such as public transport services in the same corridor. This project takes a novel approach to freight planning, using new transport and land use datasets now emerging in Australian cities. The PhD candidate will work in conjunction with a major road operator to identify and test a series of truck priority measures, from time-limited and temporary truck priority measures, through to full dedicated truck lanes and truckways, that could improve system efficiency and improve urban amenity. Suitable PhD candidates with the following background: a disciplinary background such as transport engineering, planning, economics or urban spatial analysis, with a preference for candidates with skills in Python coding, R software, geographic information systems, and/or 4-step modelling software. Research Environment: Griffith’s transport research team is highly ranked internationally, has an enviable peer-support program involving staff and PhD students, and is housed in one of Griffith’s premier buildings (the Sir Samuel Griffith Centre). This PhD will be supported by our funding agreement with the Queensland Government, including with additional conference funding support, and will likely involve a work placement with experts in a major road industry partner.

Transport planning, transport modelling, freight

BuiltEnvironment

Project Summary: It is essential that people have access to the jobs, education, goods and services they need in daily life. But many in society have less opportunity to use our transport systems. 4.4 million Australians live with a disability and transport is fundamental to their independence. The Queensland Government has the laudable aim of trying to create “an integrated transport network that is accessible to everyone”. That means being accessible and inclusive across public transport, active transport, and other modes, both within our cities and towns, and between them. Recent studies have helped identify areas for research focus, to help policies and programs make a difference. Inter-disciplinary research teams are being formed to help increase people’s independence through improvements to the built environment, transport systems, policies and funding arrangements. This project will be tailored to the particular skillsets of the candidate, to focus on a key issue for inclusive transport in the Queensland/Australian context. The PhD may include investigations into: better journey planning and information; community transport services; use of and interactions with new modes (i.e. e-scooters); personal safety and security issues; inclusive design of vehicles and stops; affordability; administrative burdens; and/or welfare outcomes. The project will work with and through local disability community advocacy groups. Suitable PhD candidates with the following background: a disciplinary background such as transport engineering, planning, economics or urban spatial analysis. Useful skills would include qualitative skills such as social surveys, interviews and focus groups; policy analysis; and more quantitative skills such as economic analyses; statistical software (i.e. R); and, geographic information systems. We are especially keen to hear from possible candidates who themselves have a disability. Research Environment: Griffith’s transport research team is highly ranked internationally, has an enviable peer-support program involving staff and PhD students, and is housed in one of Griffith’s premier buildings (the very accessible Sir Samuel Griffith Centre). This PhD will be supported by our funding agreement with the Queensland Government, including with additional conference funding support, and will likely involve a work placement with experts in an industry partner. The candidate will also be encouraged to be part of Griffith’s “Inclusive Futures: Reimagining Disability” alliance, involving key research, design, education, industry and healthcare organisations.

Transport planning, travel behaviour, disability, built environment

BuiltEnvironment

Recent literature and built projects have taught us that creating better suburbs is the key to better cities. Methods and approaches for reinvigorating suburbia abound. However, the importance of the architect’s roles and activities as an individual actor within the regulatory environment of suburbia has been widely underestimated. This is particularly the case in the context of Australian suburbs, where architects have critically contributed to enhancing its built environment throughout history. The PhD candidate would explore the significant impact of architecture as a catalyst for change in creating socio-culturally meaningful and environmentally sustainable suburbia. The candidate is expected to have an architectural design background and might need to contribute to design projects on reinvigorating suburbia.

Architecture and design, Suburban development, Sustainability

BuiltEnvironment

With the growing worldwide importance of the Global South, it is timely to explore the relationship between the past and emerging architecture in the region and understand its relationship with the construction of desired modern identities there. The PhD candidate would work on the Twentieth and/or twenty-first-century modern architecture in the Global South: in particular, the movement of ideas from the West to the East and their contribution to shaping modernism. The PhD candidate should have an architectural background and be interested in the interrelationship between modernism, architecture, architectural education, regionalism, politics, and construction of modern identities in the contemporary era.

Architecture, Global South, Modernism

BuiltEnvironment

Selection assessments are used commonly when recruiting trainee pilots for flight training or jobs and are vital to the success of the aviation industry. This study will explore the motivations of individuals to enrol and be accepted into flight training and which factors in the pilot aptitude assessments inherent in the student selection process predicts performance during flight training.

Pilot selection, personality, aptitude, ability

Aviation

The intention of pilot upset recovery training (UPRT) is to expose pilots to upsets and thereby overcome the startle reflex and the delays/errors it can generate in cognitive processing. This study will investigate the longitudinal impact of this UPRT to ascertain what the optimal refresher period for UPRT should be. The study will utilising a combination of subjective, objective and observed measures of performance to explore the intervening period required to minimise poor performance.

Pilot training, safety

Aviation

Pilot training has evolved significantly to incorporate technological advancements such as virutal reality and simulation. While virtual reality provides an additional layer of presence and immersion not previously seen in desktop simulators, the one aspect it does not incorporate effectively is motion and simulated g-forces. This study will look at what additional benefits motion adds to pilot training in the simulated environment. It will also examine which aspects of motion are essential and to what extent the motino needs to replicate the real world before motion impairs performance.

Pilot training, simulation, virtual reality

Aviation

The usage of remotely piloted aviation systems (RPAS) also called unmanned aerial vehicles (UAV, UAS) is dramatially increasing as these systems. Consequently the number of reported accidents and incidents is also increasing, as reported by national investigation agencies such as the ATSB, AAIB and NTSB. This study will investigate the human factors contributing to these accidents and incidents to determine if there are commonalities that warrant further investigation or industry education. The applicability of human factors analysis tools such as the Human Factors Analysis Classification System (HFACS) will also investigated.

RPAS, UAV, Safety, Human Factors

Aviation

Springs are surface expressions of groundwater and are often the only permanent source of water in dry landscapes. This project will use a range of novel tracers sampled in springs and groundwater to better understand sources of water and the timescales of flow to important spring systems.

Hydrogeology, Water

EarthScience

Urban environments have many sources of current and legacy contamination, and this endangers the quality of groundwater and its connected surface waters. This project will trial new and innovative methods to identify and differentiate sources of contaminants in groundwater systems, enabling better management and remediation strategies to be developed.

Water quality, hydrogeology, environmental management

Engineering

Auxetic structures are gaining attention for their adjustable properties in varying conditions, serving as a core in sandwich structures to mitigate energy transfer during impact, shock loading, crushing, and bending. These structures, conducive to personal protection equipment and protective constructs, are being developed using modern additive manufacturing. The project aims to create efficient sandwich composite with an auxetic core through novel designs, optimizing their performance using finite element and Machine Learning based tools. Numerical modelling of 3D printed parts will involve establishing a constitutive relationship implemented in ABAQUS, considering material properties at different strain rates and temperatures to enhance the efficiency of the material model. This approach enhances load-bearing capabilities and provides a predictive tool for future researchers analysing the material response of additively manufactured components.

Machine learning and material design

Engineering

In the recent years, auxetic materials have received a great deal of attention due to their unique performance characteristics under mechanical loading. Their exotic behaviour is attributed to the negative values of Poisson's ratio where a longitudinal compression (tension) is accompanied by a transverse shrinkage (expansion). In the classical theory of elasticity, Poisson's ratio ranges between −1 and 0.5 for isotropic media whereas for the anisotropic continua, there are no limits for its values. Such a vast design space offers opportunities for improved dynamic characteristics. This project aims to suggest an improved design for the core layer of the sandwich structure, taking advantage of auxtic behaviour. Finite element simulation and/or continuum mechanical modelling will be used with a possibility of incorporating physics-informed machine learning techniques.

Machine learning and material design

Engineering

Air route development (ARD) is a well-known business process within airports and airlines. Airports are usually perceived as leading partners in this stakeholder engagement, aiming to attract new and retain existing airline partners. Still, multiple other stakeholders are involved in the process, and their support or lack of it significantly impacts the sustainability of a particular route. This project aims to propose new ARD feasibility calculation methods that airports, airlines, and other involved stakeholders could use as decision-making tools. Commercial air routes are the primary focus of this project, with the potential to extend it to cargo routes.

Air route development

Aviation

The aviation industry is often seen as a symbol of globalisation, connecting people and businesses worldwide. However, despite its global reach, the industry has been slow to address issues of gender inequality. Women have been historically underrepresented in aviation, from academia to industry boards. This has led to a lack of diversity in leadership positions and a culture that can be unwelcoming to women. In recent years, there has been a growing recognition of the importance of gender equality in aviation. From initiatives to increase the number of women in pilot training programs to campaigns to promote diversity in leadership roles, the industry is taking steps to create a more inclusive environment. Despite the progress that has been made, there is still a long way to go to achieve gender equality in aviation. By continuing to push for change and challenging the status quo, this research aims to explore avenues that will lead to a more inclusive future for aviation.

Gender equality

Aviation

Flight decks are constantly evolving. New technology is constantly implemented in it to enhance safety. However, with such new additions there are also some challenges that might arise. These need to be understood to maintain safety. Touch screen technology is one of the newest additions in the flight deck. In this project, the interaction with such technology during various flying conditions will be explored. Any issues pilots might face when interacting with such technology will be understood. Training required to know when to use and when not to use touchscreen technologies will be examined too. As seen with many other types of flight deck technologies; a new piece of technology that starts off in the commercial airline industry might trickle down into the general aviation industry also. Hence, suitable recommendations will be made for the wider aviation industry.

HF

Avionics

Electric power systems are considered critical infrastructure and are susceptible to various contingencies such as natural disasters, system errors, and malicious attacks. These contingencies can have a severe impact on the world's economy and cause significant inconvenience to our daily lives. Hence, the security of power systems has been a topic of considerable interest for decades. With the recent development of the Internet of Things (IoT), power systems can support various network functions throughout the generation, transmission, distribution, and consumption of energy through IoT devices like sensors and smart meters. However, this has also led to an increase in security threats. Cascading failures are one of the most severe problems in power systems and can result in catastrophic impacts such as widespread blackouts. Furthermore, these failures can be exploited by malicious attackers to launch physical or cyber attacks on the power system. This project aims to investigate cascading failure attacks and develop AI techniques to detect and defend against them. Feel free to contact me for further discussion.

Deep Learning, Data and Information Privacy, Cybersecurity

ITScience

Electroencephalography, or EEG for short, is a technique that measures the electrical activity of the brain through electrodes placed on the scalp. This non-invasive and cost-effective technique has been used in various fields, including neuroscience research and clinical practice. One of the main reasons why EEG research is so important is because it allows us to gain valuable insight into brain function and dysfunction. It has been extensively used to investigate a wide range of cognitive processes, such as attention, perception, memory, language, and emotion. Furthermore, EEG has been instrumental in diagnosing and monitoring neurological disorders, including epilepsy, sleep disorders, and traumatic brain injury. Overall, EEG research is an essential tool for understanding the human brain and its disorders. This project focuses on research on EEG biometrics and its applications.

Deep Learning, Data and information privacy, pattern recognition

ITScience

The use of 2D fingerprint technology has become widespread in various authentication applications, such as mobile phones, laptops, and building access control. However, this technology has limitations, as it cannot fully replicate a real finger and is vulnerable to spoofing attacks. As a result, there has been a shift towards the development of 3D fingerprint biometrics, which offers benefits such as hygienic, contactless, anti-spoof, and natural representation. The aim of this project is to explore 3D fingerprint biometrics and to develop an AI-guided 3D fingerprint biometric system. The project will involve the application of AI/deep learning on 3D point cloud data. Feel free to contact me for further discussion.

Knowledge representation and reasoning, Pattern recognition, Deep learning, Data and information privacy

ITScience

AI-based human face recognition is a mature technology and has been adopted in many applications, such as mobile phone. However, recognition of animal face is still an under-investigated topic. Leveraging the success in human face recognition technology, this project aims to develop novel koala face recognition methods based on transfer learning using images and videos captured in the natural environment. The research outcome will leave to innovative tools for koala population estimation and conservation.

Computer Vision, Machine Learning

ICT

Advanced cyberattacks pursue their victims over months or years until they can reach their final goal. Detecting these threats in early phases before the final stage of attacks can be executed against endpoint devices can help prevent adversaries from achieving their goals. Many organisations use cyber threat hunting to proactively detect hidden intrusions before they cause a major breach. The goal of hunting is detecting threat actors early in the cyber kill chain by searching for signs of an intrusion and then, providing hunting strategies for future use. An emerging method in cyber threat hunting is using Natural Language Processing (NLP) methods to automate the hunting process. This project aims to investigate and develop practical threat hunting approaches using NLP methods. This method can be used to automate the extraction of indicators of compromise (IoCs).

Cybersecurity

Technology

With the emergence of the Internet of Things (IoT) and Industry 4.0, there is a trend for applying these services and applications in a large-scale industrial area. The IoT paradigm has changed the way of interactions with the things that surround us. In essence, the IoT promises ubiquitous connection to the Internet, turning common objects into connected devices. This project will review the different architectures of IIoT, and systematically study the security challenges associated with interoperability, access control, privacy, and trust-related issues, in general. The project will also identify the gaps in the state-of-the-art security techniques and requirements to determine the security services in the IIoT environment and propose potential mitigation techniques to address these gaps.

Cybersecurity

Technology

Modern Intrusion Detection Systems (IDSs) rely on machine learning for detecting and defending cyber-attacks in information technology (IT) networks. However, the introduction of such systems has introduced an additional attack dimension; the trained IDS models may also be subject to attacks. The act of deploying attacks towards machine learning-based systems is known as Adversarial Machine Learning (AML). The aim is to exploit the weaknesses of the pre-trained model which has “blind spots” between data points it has seen during training. More specifically, by automatically introducing slight perturbations to the unseen data points the model may cross a decision boundary and classify the data as a different class. As a result, the model’s effectiveness can be reduced as it is presented with unseen data points that it cannot associate target values to, subsequently increasing the number of misclassifications. Adversarial machine learning attacks and automated detection of these attacks in computer networks will be investigated in this project. This project aims to investigate adversarial attacks to machine learning in cybersecurity of cyber-physical systems and propose mitigation techniques to defend against these attacks.

Cybersecurity

Technology

Cyber threat intelligence (CTI) is the knowledge about a threat, and it includes threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, etc. CTI can help organizations to learn about existing threats. Cyber threat intelligence can be received from external open-source threat intelligence, or it can be extracted from adversarial activities in organizations’ networks. The CTI generated will be used to build intelligence about threats against a given target. In cyber threat intelligence, indicators of compromises (IoCs) are generated. These IoCs of the detected adversary can be processed and distributed. Security analysts to use CTI information from other companies and share back their IoCs with other trusted partners. These shared IoCs can be used to update detection rules and blacklists in security devices like firewalls. This project will review state-of-the-art techniques CTI sharing and identify gaps in the current solutions. It will also investigate how threat intelligence can be automatically created and shared for new emerging attack and link the CTI to cyber security defence mechanisms.

Cybersecurity

Technology

Microgrids provide a flexible architecture for deploying distributed energy resources that can meet the wide range needs of different communities from metropolitan cities to rural country areas. This project aims to develop new control and optimisation technologies to implement self-scheduling and self-coordinating among all microgrids in a networked microgrid. It provides a feasible solution for the challenge of both the growing number of microgrids and high penetration level of renewable energy in a power grid. The outcomes of this project will promote the increase in the renewable energy fraction of the total electricity supply in Australia and worldwide.

Electrical Engineering

Engineering

One of the greatest challenges to airline flight safety, is how well the pilots diagnose and respond to complex abnormal or emergency situations, such as multiple failures, false alarms, and inoperative systems, which can arise during flight of modern aircraft. Aircraft have evolved to become technically highly sophisticated, becoming more ‘robot than machine’, but approaches to pilot support in the modern cockpit lag well behind. Pilot support in the cockpit remains limited to traditional approaches (the checklists and procedures of the Quick Reference Handbook), effectively leaving pilots unsupported, 'mostly on their own’ to deal with complex, safety critical situations. The aim of the research is to harness areas such as AI/reasoning/machine learning alongside research in Human Computer Interaction to develop new approaches to supporting pilot decision making, thereby enabling pilots to diagnose and respond more safely and effectively to the complex abnormal or emergency situations that they will encounter when flying modern aircraft.

AI reasoning machine learning DSS/HCI

ITScience

Data streams are sequences of data that are continuously transmitted to a receiver. Outlier detection is to identify abnormal data, or data that are significantly different from normal data. The problem of detecting outliers from data streams has important applications and has attracted a lot of attention from researchers. However, there are still many challenges in accurately and efficiently identifying outliers, that is, how to effectively distinguish normal data from outliers, and how to achieve real-time identification. This PhD project aims to develop novel techniques for the problem.

Data Management and Analysis

ITScience

Graph data are ubiquitous nowadays. Real-world graphs (e.g., social network graphs, knowledge graphs, road networks) are getting larger and larger, which makes many common graph queries (e.g., subgraph matching/counting, crucial nodes/edges identification, cohesive subgraph computation, constrained shortest paths) time-consuming. However, in many real-world applications, approximate answers are sufficient and much easier to find. This PhD project aims at developing novel techniques for the fast finding of approximate answers, focusing on subgraph computation/counting queries.

Data Management and Analysis

ITScience

Micro- and nano-plastic debris in aquatic, terrestrial and marine habitats have become significant concern for human health. Due to their tiny size, developing a high-throughput system to detect and classify them is a challenging task. It has been proven that shortwave hyperspectral imaging technology is highly effective in classifying plastics in the size of tens of micrometers. Nevertheless, when the size of plastics reduces to sub-micrometer or nanometers, traditional hyperspectral microscopic system becomes infeasible. This project aims to develop an innovative technology for micro/nano-plastic detection and classification using dark-field hyperspectral microscopy. The scope of the project includes hyperspectral image capture with dark-field microscopy, image processing, and machine learning method development for particle detection and classification. The student will work with a multidisciplinary team in ICT, Environment, Material Science, and Mechanics. 

Image Processing and Computer Vision

ComputationalScience

This Project aims to investigate the mechanism that integrates local search and complete search, and machine learning for real-world applications. This project will develop the strategies for the cooperation of local search and complete search in solving hard problems from real-world. It will explore the cooperation of local search and complete search for training deep neural networks. On the other hand, this project will propose novel mechanism to design local strategy to by using machine learning technologies. The aims of this Project include both the novel paradigm for training deep neural networks and efficient algorithms with the cooperation of local and complete search strategies.

AI

ITScience

Blockchain is a promising technology towards achieving full-scale digital transformation in a complex environment. This technique has attracted a number of successful applications such as cryptocurrency, supply chain, trade finance and smart contracts. Blockchain has been showcased as a game changing national technological strategy in several countries. This project will extend our current work on the classification of digital assets, cross-chain integration protocols, and formal verification of smart contracts with novel design patterns and formal security guarantees for inter-blockchain systems. Experiments and validation will be carried out on test-networks and using real-world case studies with our industry collaborators

Data Format

ICT

Pre-existing research has failed to offer a solution to protect patients’ privacy and confidentiality, it is important to identify the limitations of existing solutions and envision directions for future research in privacy preservation in health informatics. This research aims to identify current outstanding issues that act as impediments to the successful implementation of privacy measures in health informatics and the limitations of available solutions. Feasibility of using blockchains for dealing with health and medical will be researched and evaluated. Then, propose a privacy-preserving framework by improving data storage, record linkage techniques.

Computer software

ICT

This project focuses on various methods that are used for the recommender systems based on social networks. Students will explore research issues in recommendation algorithms, and gain experience in applying appropriate methods to predict user preferences in different settings. It is to form the in-depth analysis of data-driven behaviors strongly interdependent with each other. Students will need to propose recommendation solutions for social network users and evaluate the prediction accuracy after applying the methods. Students will explore research issues to design the underlying models and algorithms for those heterogeneous and interdependent behavioral data to make predictions and recommendations, as well as develop software prototypes.

Computer Science and AI

ICT

This project aims to develop an advanced system for automatically identifying and flagging fake news articles using large language models. Leveraging the capabilities of large language models like GPT, this project involves preprocessing a diverse dataset of news articles, extracting meaningful features, and training a classifier to distinguish between genuine and fake news. This project will explore fine-tuning techniques to enhance the model's adaptability to different domains and evolving forms of fake news. The ultimate goal is to deploy a robust fake news detection system capable of assisting in the ongoing battle against misinformation and safeguarding the integrity of online information dissemination.

Computer Science and AI

ICT

This project proposes to develop advanced AI models capable of providing deep insights into complex interdependencies and heterogeneous behaviors observed across multiple data views. By leveraging state-of-the-art deep learning techniques and interpretability methods, the project seeks to unravel intricate relationships and patterns within multi-view data sources, enabling a comprehensive understanding and explanation of how diverse factors interact and contribute to observed behaviors. Through rigorous experimentation and analysis, the project aims to enhance the transparency and interpretability of AI models, facilitating informed decision-making in various domains such as healthcare, finance, and social sciences.

Computer Science and AI

ICT

This project aims to research into the application of AI to assist with learning and teaching (L&T). There are many aspects of L&T that can benefit from the use of AI. However, instead of having AI playing a central role, e.g. run a class, this research focuses on the use of AI in a supporting role such as providing feedback to students, assist teachers in marking students work, or even directly mark student’s formative work etc. This applied research designs and develops AI tools to assist in specific L&T activities, applies these tools and evaluate the results. It explores the scenarios where AI tools are of benefit and determines how the tools should best be utilised.

AI, learning and teaching, computer software, machine learning

Technology

AI-powered recommender systems provide recommendations for daily lives, but they need to be legally interpretable and explainable. This project aims to transform existing black-box recommender models into transparent and trustworthy decision-support systems. The resulting tools will offer granular, explorable rationales for the recommendations in real time, creating greater public confidence while advancing the field. The expected outcomes include graph embedding methods for capturing real-world relationships in all their messiness and complexity. The anticipated contributions include impartial and accountable recommender models that are resistant to adversarial attacks and that slow the spread of misinformation.

Trustworthy AI, Privacy Preserving Computing

ICT

With the evolution of modern information technology, medical data sharing has become a part of our daily life, which greatly benefits medical research and development professionals, service providers and the society as a whole. The rapidly growing volume and variety of medical data in the past decade has made privacy protection an increasing concern to data owners, obstructing medical data sharing to reach its expected scientific and market value. This project will focus on a solution to provide an adaptive protection for medical data so that data users can create the explainable intelligence based on the shared data without disclosing any privacy information.

Privacy Preserving Computing, networks

ICT

With the exponential growth of streaming data from various sources in both volume and content, privacy protection for streaming data and their secure analysis are becoming increasingly important. Considering the properties of streaming data including mass volume (unbounded size), heterogeneity, dynamicity, concept drift and feature evolution this project applies multi-fold theories and techniques including secure computing, privacy protection, machine learning, intelligent searching, data mining in an effectively coordinated way. The project first studies how to discover and measure sensitive information of data instances, including features and labels, in data streams. It then investigates suitable models, schemes and mechanisms for effective protection of the sensitive information while preserving the required data utility. Finally it develops new techniques and methods for various privacy-preserving streaming data analysis and mining, including statistical analysis, association mining, classification and clustering, and evaluation of their performance.

Privacy Preserving Computing, networks

ICT

Conductors in powerline corridors are thin, so only a small number of laser points get reflected, which makes it difficult to effectively extract the conductors using the aerial point cloud data. We have recent success in extraction of the conductors even when conductors are in bundles of 2 or 4 sub-conductors. However, power line corridors often exist in complex environments (e.g., forests), where occlusions, missing data and noise are regular phenomena. The advancement of recent deep learning techniques will be useful for high-performance powerline corridor segmentation in complex environments.

Artificial Intelligence

EnvironmentalScience

Essential powerline components such as conductor, cross arm and insulator will be periodically extracted and their properties (narrowing of diameters, broken discs, sizes, material fatigue) will be estimated using machine learning techniques combined with a statistical analysis. Also, faults in these components will be automatically detected and managed. Along with point cloud data, multispectral, hyperspectral as well as thermal imagery could be used for these purposes.

Artificial Intelligence

EnvironmentalScience

The 3D object reconstruction is highly challenging due to high data complexity, structural variations, presence of noise, and missing of data. Buildings come in different shapes and their unique architectural designs pose a great challenge, specifically for extraction and modelling of small components such as chimneys. The recent deep learning architectures have shown high success in object-part segmentation, e.g., a plane can be divided into wing, body, fin, and stabilizer. So, prior knowledge about a building roof style can be sought as a prerequisite step for building roof reconstruction. The project aims to employ deep learning architecture to segment a roof into parts and then classify them into roof styles before the roof is reconstructed as a complex shape.

Artificial Intelligence

EnvironmentalScience

Large language models (LLMs) are disruptive tools for software engineering and many other industries. In particular, people have used LLMs to develop automatic programmers. However, due to hallucination and many other issues of generative AI, existing solutions often give incorrect answers, and the resultant program cannot be used in critical applications. This project aims to integrate formal methods, such as program refinement techniques, with LLM to produce next-gen automatic programmers that can guarantee the correctness of produced programs.

Programming Languages

ICT

Software testing can only show the presence of bugs but never their absence, so it is crucial to mathematically prove the correctness of programs in mission-critical domains such as aerospace, defence, finance, health, etc. This practice is called formal (program) verification. The advancements in quantum computing extend the application of formal verification to quantum programs, which is uncharted territory. This project will develop new verification techniques that are suitable for quantum programs, possibly using quantum computing algorithms.

Quantum Computing

ICT

Complex formal proofs require significant effort and tools such as interactive theorem provers, whereas automated reasoners are often limited by the size of the problem or computational time. Recent advancements in machine learning and AI have led to new tools, such as Sledgehammer, that use traditional theorem provers in smart ways to improve run time and automation. New algorithms such as HyperTree Proof Search applies ideas from Monte-Carlo Tree Search and deep reinforcement learning to push the boundaries of state-of-the-art theorem proving. This project aims to extend the above ideas to develop new large language model (LLM)-based tools for Isabelle/HOL that can reason about logical formulae faster and more automatically.

Logic, AI, Automated Reasoning, Machine Learning

ComputationalScience

This project focuses on developing explainable AI solutions to decision support systems, by combining knowledge graphs, machine reasoning and machine learning. Knowledge graphs is a promising data and knowledge organisation, synthesis and management approach, and we have developed scalable reasoning tools for knowledge graphs coupled with ontological rules that describe domain knowledge or business rules. This project aims to study the problem of incorporating such high-level knowledge and formal reasoning in the analysis of cross-media data. Moreover, such knowledge and reasoning can be integrated with machine learning models to provide powerful support for informed decision-making where a justification or explanation of the decision can potentially be retrieved.

Artificial Intelligence Image Processing

ICT

This project aims to develop novel stream learning algorithms for continuous patient outcome prognosis by taking into account patient's data collected during ICU admission in a unified manner. The algorithms are expected to integrate high frequency time series data with patient's demographic data, lab data, diagnosis data, prescription data, etc. as exemplified in MIMIC-III, for accurate outcome prognosis. Issues such as prediction bias, data leakage, data sparsity, non-stationarity, model explainability will be investigated.

Information Systems

ICT

Due to the “black box” characteristics of the deep learning technique, the deep network-based computer-aided diagnosis systems have encountered many difficulties in practical application in healthcare. The crux of the problem is that these models should be explainable – the model should give doctors rationales that can explain the diagnosis.The objective of this project is to research on highly interpretable algorithms to generate "trust" between the human users and the algorithm, designing user-friendly explanations and developing comprehensive evaluation metrics to further advance the research of interpretable machine learning in biomedical image analysis.

Artificial Intelligence Image Processing

ICT

This project explores the relationship between reinforcement learning (RL) and probabilistic model checking (PMC), as both are built upon the underlying model of Markov decision processes. On the one hand, PMC may be used to guide and constrain an RL agent when exploring optimal solutions so that the agent operates within a "safe region". On the other hand, RL may be used to improve the performance of model checking algorithms through statistical methods. We aim to improve the state-of-the-art of both worlds.

Artificial Intelligence, Automated Reasoning

ICT

We have recently developed state-of-the-art techniques for sports video processing using deep learning and strategy and match outcome anlaysis using probabilistic reasoning. This project aims to extend these results to deal with different sports, such as soccer, baseball, basketball, etc. We are also interested in developing applications for match analysis, visualisation, match outcome simulation and so on. The current methods may be combined with large language models to provide smart responses to user queries.

Artificial Intelligence, Automated Reasoning

ICT

Machine learning is one of the hottest topics in computer science, but it is often used as a "black box"; consequently, the trained model may behave unexpectedly and yield catastrophic results. This project aims to develop new methods to understand machine learning models. We will adopt various techniques that synthesise different forms of "explanations" as approximations of the original machine learning models. We will evaluate these methods for a variety of machine learning algorithms, including CNN, RNN, random forest, and reinforcement learning. As a case study, we will look into modelling the abstract state machines in Process Analysis Toolkit (PAT) and develop an interactive query system that allows the user to ask questions about machine learning models and get answers. We will also investigate how to present the interactive system in a user-friendly interface.

Artificial Intelligence, Automated Reasoning, Machine Learning

ICT

Respiratory infections and antibiotic resistant bacterial infection are some of the conditions that have significantly stressed our hospital ICU. A number of risk factors have been reported to associate with severe diseases, which includes age, pre-existing conditions, pathogen setpoints, responsiveness to therapeutic strategy. Any single risk factor is unlikely to be an absolute determinate of clinical diseases, rather many contributors or associations are important with the disease progress. We will use ICU data and artificial intelligence to generate a prediction algorithm to assist clinical decision making.

Artificial Intelligence Image Processing

ICT

Intelligent systems are important for cross-media (e.g., text, image, video, and audio) retrieval, web content monitoring, web information trend analysis, and healthcare data fusion. The state-of-the-art AI approaches to cross-media analysis mostly adopt a black-box neural networks approach, which cannot provide human-understandable explanations for specific decisions made. This research aims to develop explainable AI techniques for analysis and reasoning of cross-media contents by integrating deep learning with symbolic knowledge representation. This project is based on the unique strength of the supervisors' research group and is a critical part of a proposed future ARC project.

Artificial Intelligence Image Processing

ICT

Big Data Analytics, AI and Blockchain

ICT

In today’s digital ecosystem world, we see more and more intelligent devices are connected over the Internet, enabling them to share their data on the Web. This allowed us to collect a large amount of data and use then for intelligent situation awareness and intrusion detection. This research will address the key issues facing to cyber security: 1) big streaming data analysis, 2) the huge number of generated alarms with the vast majority being false alarms, 3) human effort to investigate alarms to find intrusions, 4) determination and removal of false alarms, 5) timely decision making in a constantly changing environment, and 6) the ability to capture previously unknown attacks.

Big Data Analytics, AI and Blockchain

ICT

Crop yield estimation plays a significant role in management of agricultural activities and decision making, such as fertiliser (e.g., nitrogen) use, crop insurance, harvesting and storage requirements, and budgeting. Visual yield assessments by growers or agronomists could be highly subjective and labour intensive. Methods based on satellite RGB image along with existing agronomic and meteorological data compute in-season vegetation indices often estimate yield for an entire district or region, so they lack in farm-specific yield estimation. Moreover, sugarcane yield estimation does not depend much on the 2D spectral information (i.e., leaf-colour), rather on the plant height and stalk density. Thus, the project will investigate the use of 3D point (aerial laser scanning) data for accurate sugarcane yield estimation. These data will provide important cues to estimate number and density of sugarcane stalks.

Artificial Intelligence

EnvironmentalScience

Traditional control techniques have limitations when it comes to ensuring that a swarm of autonomous agents (whether fully or partially automated) fulfill their tasks, while at the same time observing the rules of engagement. The project will explore the possibilities of command rather than control over such multi-agent systems. (The project suits someone who is eligible to work for Australian Defence). Multi-agent systems simulation would also be required.

Artificial Intelligence, Explainable AI

ICT

Current AI/ML techniques are limited in terms lacking meta-cognition that allows a system to reason about its own abilities and capabilities in light of the problem space encountered. The project would suit a candidate who is interested in both the theory of AI and in experimenting with implementation tools to build efficient and effective AI systems (e.g. SOAR, CLARION, ACT-R,...) as well as LLMs.

Artificial Intelligence, Cognitive Science

ICT

Coordinated action by multiple agents (such in robotic swarms), is an important area, especially whe there is limited or only intermittent communication. This requires both local planning and adjustment when there is a possibility to coordinate, so that the swarm as an emergent agent can fulfill an overall intent. The work would involve literature review, theory building, and validation through simulation experiments (using a multi-agent simulation platform).

Multiagent systems

Computing and simuation

Hyperspectral videos contain rich spectral, spatial and temporal information of moving objects. The goal of this project is to develop fundamental hyperspectral image analysis, object detection and tracking methods and explore their applications in agricultural, environmenal, and medical applications.

Computer Vision and Machine Learning

ICT

In both real and simulated environments, people can be overwhelmed if exposed to high levels of competing stimulus which can negatively impact cognitive load. When using immersive technologies, for example for augmented reality, there are opportunities to add useful virtual objects into a real-world environment both as objects located in 3D space and as part of an extended user interface (UI), i.e., a head-up display (HUD). These elements need to be managed as not to diminish user experiences. This project will explore intelligent agents (AI support) to enable user-initiated UI adaptation and pre-empt the need for UI changes based on user, task and environment contexts, for example monitoring physiological sensors for indications of user distress (cybersickness, heart-rate increase) and/or contextual cues in real/virtual environments that may indicate that there is a need to reduce any augmented stimulus and/or dynamically reconfigure the user’s current user interface.

Human-Computer Interaction

Technology

Virtual environments in the Metaverse provide almost infinite visual real estate for interacting with 3D data visualisations. This provides opportunities for novel deployment of large data sets across diverse domains, including health, climate, education, defence, cybersecurity, blockchain, etc. However, there are significant challenges to useful data engagement in the Metaverse where information and users may be distributed across environments but still need to collaborate. This project will explore the visualisation of 3D data using immersive head-mounted displays that support eXtended Reality (XR) and develop new paradigms for virtual and real-world data interaction into, within and out of the Metaverse.

Virtual Reality

Technology

Graph machine learning, graph neural networks, in particular, is the frontier of deep learning. There has been an exponential growth of research on graph neural networks (GNNs) in the last few years, mainly focusing on how to develop accurate GNN models. The trustworthiness of GNNs is less considered. In this project, we will explore how to develop trustworthy GNN models. The key aspects, including robustness, explainability, fairness, and privacy, will be taken into consideration when developing GNN models.

Machine learning, Data Mining, AI

ICT

Knowledge graphs are important tools to enable next generation AI through providing explanation for different applications such as question answering. Knowledge graphs are typically sparse, noisy, and incomplete. Knowledge graph reasoning aims to solving this problem by reasoning missing facts from the large scale knowledge base. This project aims to develop novel scalable technique for knowledge graph reasoning. The developed techniques will be further generalised to more general graphs with graph neural networks.

Machine Learning, Data Mining, AI 

ICT

Graph neural networks (GNNs) are emerging techniques for AI. As many chemical compounds and proteins in biology can be modelled as graphs, GNNs have great potentials for drug discovery. This research will investigate new GNN based techniques to accelerate the process of drug discovery.

Machine Learning, Data Mining, AI

ICT

Many companies have the privacy policy set for the data they collected. Due to the evolution of AI-based technology, how AI shall be used to help with an automated privacy impact assessment?

Privacy Preserving Computing, networks

ICT

Cyber threat intelligence (CTI) refers to knowledge about potential threats, which includes information on threat indicators such as Tactics, Techniques, and Procedures (TTPs), IPs, and more. CTI can help organisations identify existing threats, either through external open-source threat intelligence or by monitoring adversarial activities within their own networks. The generated CTI can be used to build intelligence about threats against a specific target. Initially, indicators of compromise (IoCs) are generated, and these IoCs can be processed and shared using CTI sharing techniques. Such techniques allow security analysts to use CTI information from other companies and share their IoCs with trusted partners, which can be used to update detection rules and blacklists in security devices such as IDSs and firewalls. To enable effective and collaborative cyber threat intelligence sharing, the application of state-of-the-art machine learning techniques in the CTI generation and sharing should be investigated. This project will review automation of CTI generation and sharing using machine learning . The efficacy of using machine learning technology for detecting network attacks has been widely studied, but it has been difficult to create an ML-based detection system that can handle diverse network data samples from different organisations. This project aims to propose an automated cyber threat intelligence sharing using machine learning that enables multiple organisations to work together to share their IoCs.

Cybersecurity

Technology

The progress made in fields such as the internet of things, artificial intelligence, machine learning, and data analytics has facilitated the development of digital twin technology. A digital twin is a high-fidelity digital model of a physical asset or system that can be utilised to optimise operations and predict faults of the physical system. Operators of cyber-physical systems need to be aware of the cyber situation in order to adequately address any cyber attacks in a timely manner. Early detection of cyber threats can quicken the incident response process and mitigate the consequences of attacks. However, gaining a complete understanding of the cyber situation may be difficult due to the complexity of cyber-physical systems and the ever-changing threat landscape. More specifically, cyber-physical systems (CPSs) usually have to be continuously operational, and they may be sensitive to active scanning of the network traffic. Digital twins can address these challenges by providing virtual replicas of physical systems that can be analysed in-depth without disrupting operational technology services. This project aims to assess the usefulness of digital twins for the cybersecurity of cyberphysical systems and review the tools and technologies available for creating them. Additionally, a cybersecurity framework for anomaly detection using digital twins in cyber-physical systems will be proposed.

Cybersecurity

Technology

Over the past few years, many intrusion detection systems (IDSs) have been developed using machine learning methods. These automated IDSs can automatically analyse network data, including network traffic and device logs, to detect intrusions. Cybersecurity experts rely on these systems' recommendations to improve network security. To enhance the reliability of IDSs, it is important that the decisions made by these machine learning-based solutions can be justified to humans. However, the current automated IDSs are used as black boxes, providing no information about the reasons behind their predictions. It should be clear to cybersecurity experts which features of the network data caused the intrusion. This project aims to identify state-of-the-art techniques to develop an explainable IDS, addressing this gap and providing a better explanation of IDS decisions. It will investigate how existing methods can be improved to provide more comprehensive interpretability of machine-learning-based IDSs and provide details about the features involved in IDS decisions.

Cybersecurity

Technology

Modern Intrusion Detection Systems (IDSs) play a vital role in safeguarding information technology (IT) networks against cyber-attacks. IDSs rely on machine learning techniques to analyse network traffic and identify suspicious patterns and anomalies that may indicate an ongoing or impending attack. However, the deployment of these machine learning-based systems has introduced an additional vulnerability, as the models they use to detect and respond to threats may also be subject to attacks. This type of attack is known as Adversarial Machine Learning (AML) and involves exploiting the underlying machine learning algorithms to evade detection, misclassify data, or manipulate the training process. This project will study AML techniques in cybersecurity domain and propose defence strategies.

Cybersecurity

Technology

As the use of connected devices and the Internet of Things (IoT) becomes more prevalent in manufacturing processes in the Fifth Industrial Revolution (Industry 5.0), cybersecurity becomes a critical consideration. The integration of these devices presents new opportunities for cybercriminals to exploit vulnerabilities and attack the system, hence organisations must implement robust cybersecurity measures to safeguard their data and systems. A significant cybersecurity challenge in Industry 5.0 is protecting the data generated by connected devices. This information is often confidential and valuable, and unauthorised access to it can disrupt operations or result in intellectual property theft. To counter this risk, it is crucial to encrypt, securely transfer and store data to prevent unauthorised access. The project will study the new threat landscape in industry 5.0, and propose mitigation strategies for the new vulnerabilities introduced by the Fifth Industrial Revolution.

Cybersecurity

Technology

As the number of advanced cyber attacks is rapidly increasing in the modern world, it is crucial to detect attacks as soon as possible to prevent them from reaching their final goal and causing destructive damage. Therefore, a robust cybersecurity system is needed to detect and respond to potential cyber-attacks in a timely manner. Although automated intrusion detection using artificial intelligence has been proposed by many researchers, the performance of these methods still needs improvement. This project aims to review the applications of optimisation algorithms, such as the whale optimization algorithm (WOA), in improving the performance of networks (ANN)-based solution to detect cyber-attacks. Different optimisation algorithms will be analysed and compared to find the method that can outperform other methods.

Cybersecurity

Technology

Many real-world problems have multiple, conflicting objectives and/or constraints that are dynamic in nature. These problems are reffered to as dynamic multi-objective optimisation problems. Nature-inspired population-based algorithms enable natural parallel search for a set of optimal trade-off solutions. This study will investigate how a decision maker's preferences can be incorporated in the dynamic search, to focus the search around more preferred regions of the optimal trade-off solution set in an interactive and dynamic way. The algorithms will be applied to disaster management and recovery.

Artificial intelligence, Optimisation, Computational Intelligence

ICT

While supervised learning is known for a while introduction of pretrained transformers reduced the demand and therefore the cost for annotation of training data. Pretrained transformers learn from large amounts of unlabeled text data and are a form of Large Language Models (LLM). Another milestone in AI was the introduction of the Generative Pretrained Transformers (GPT) framework, which has a decoder layer and is able to understand and generate human-like text. The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. For example, popular ChatGPT3.5 was trained with data generated prior to January 2022. There are approaches to finetune smaller base LLM with domain-specific data, however, further improvements are needed to improve accuracy, reduce hallucination and ensure information generated from such models is up-to-date.

Computer Science and AI

Computational Science

The popularity of OpenAI ChatGPT revolutionised the GenerativeAI. While these models provide huge benefits training such models is time-consuming and costly, which causes the information in such models to be not up to date. Proposed methods to finetune smaller base LLM with up-to-date data do not necessarily discard irrelevant outdated data from Large Language Models. This work will look into options to ensure finetuning of the models forgets only desired parts and does not cause catastrophic forgetting due to multiple model fine-tuning.

Computer Science and AI

Computational Science

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of backdoor attacks, which involve stealthily implanting malicious features or patterns during model training, enabling unauthorised access or manipulation of the neural network's behaviour under specific conditions, compromising its integrity and functionality. Notably, recent reports have raised suspicions about a significant number of pre-trained DNN models from Model Zoo being vulnerable to backdoor attacks. This project investigates backdoor attacks on DNNs, aiming to expose attack mechanisms, assess real-world implications, and propose detection/mitigation strategies for robust AI systems. The exploration includes defining and elucidating backdoor attacks, examining implantation techniques, showcasing instances, and consequences, and analysing recent cases for lessons.

Cybersecurity, AI, Optimisation, CV or NLP

Computational Science

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their security. One particularly insidious threat is the emergence of adversarial attacks, wherein malicious actors deliberately manipulate input data to deceive deployed DNNs, leading to misclassifications or compromised performance. These attacks pose a significant challenge, demanding a comprehensive understanding of their mechanisms and the development of robust defences. This proposal aims to delve into the intricacies of adversarial attacks, exploring their impact on DNNs and proposing effective strategies to fortify these models against such sophisticated threats.

Cybersecurity, AI, Optimisation, CV or NLP

Computational Science

In the era of rapidly advancing artificial intelligence, deep neural networks (DNNs) have become indispensable tools for various applications. However, as the reliance on these models grows, so does the concern over their privacy leakage. Notably, instances such as ChatGPT inadvertently revealing its training data through deliberately designed prompts underscore the pressing need to address privacy vulnerabilities in DNNs. This proposal seeks to delve into the vulnerabilities of DNNs to privacy attacks, examining potential threats stemming from learning paradigms, model architectures, training data, training processes and inference outputs. By understanding these risks, the project aims to develop robust privacy-preserving mechanisms and effective defences against privacy leakage, ensuring that the deployment of DNNs aligns with stringent privacy standards.

AI, Optimisation, Secure Multiparty Computation, Differential Privacy, CV or NLP

Computational Science

This project aims to improve understanding of stingless bee nutrition, hive propagation, colony health, and pollination efficiency in a range of horticultural cropping environments and surrounding landscapes.

Stingless bees, avocado, lychee, macadamia, strawberry

Biology and Horticultural Science

This project aims to use biochar to develop new generation of fertilisers. The novel fertilisers decrease farm nutrient inputs and increase farm yield.

Soil, environment and agriculture

soil sciences

This project aims to decrease food waste and farm inputs using AI – needs skills in computer sciences and image processing

Computer Science and AI

computer sciences and image processing

Parasites including Plasmodium (causes malaria) and Toxoplasma gondii (causes congenital birth defects) lack effective vaccines and drug treatments remain the only therapy, yet few exist, and drug-resistance limits their use. PROTACs are an emerging technology, which show great promise in limiting drug resistance in a range of human conditions and are in clinical trials, yet there is a lack of research into PROTAC-mediated targeting in parasites. In this proposal we will address this shortcoming and provide a pathway to augment and limit resistance of current and emerging drug therapies, for these parasites with PROTACs.

Chemical Biology, analytical chemistry

ChemicalSciences

Currently mRNA manufacture is expensive, slow, can result in impurities and a product with low stability. In principle, the chemical synthesis of mRNA could overcome these limitations. However, current solid phase RNA synthesis methodology is restricted by the length of RNA it can produce. This project aims to develop a strategy for the solid phase chemical synthesis of mRNA, overcoming all limitations of current synthesis to transform the way mRNAs are made. This is expected to deliver significant improvements in RNA sequence length, quality, incorporated modified bases and capping groups than is presently possible. The new synthesis will be applied to produce stable peptide-encoding mRNAs.

Chemistry, Chemical Biology

ChemicalSciences

Diffusion models, a recent addition to deep generative models, have shown exceptional performance across diverse applications such as image generation, audio synthesis, and scientific research. Despite their promising results, challenges hinder their broader adoption in scientific research and applications. These challenges encompass computational demands, interpretability issues, data prerequisites, uncertainty quantification, and concerns regarding biases and precise control over the generation process. This project is dedicated to overcoming these barriers to unlock the full potential of diffusion models in scientific research and applications.

AI, Computer Vision, Machine Learning, Deep Learning

ICT

A foundation model serves as a robust machine learning backbone trained on extensive datasets, adaptable for diverse applications and tasks downstream. However, fine-tuning them is resource-intensive. This project endeavours to innovate efficient fine-tuning methods to mitigate this challenge.

AI, Computer Vision, Machine Learning, Deep Learning

ICT

Vision Transformers (ViTs) represent a recent breakthrough in computer vision, adapting the Transformer architecture from natural language processing (NLP) to image recognition tasks. ViTs have garnered attention for their capability to capture long-range dependencies in images without using convolutional operations. However, achieving robustness and generalisation with ViTs poses challenges. Additionally, developing efficient transformer models tailored for computer vision remains an open problem. This project aims to explore novel transformer architectures, algorithms, and techniques to address these limitations.

AI, Computer Vision, Machine Learning, Deep Learning

ICT