phd research proposal artificial intelligence

Artificial Intelligence Research Proposal

Artificial intelligence or AI is one of the latest technologies being used in integration with machine learning, deep learning, and deep reinforcement learning. Developers and software designers craft solutions to some of the important problems in AI . Data or information in the form of digital satellite images, visual data, structured unstructured, and text data. Artificial intelligence research proposal writing needs expert advice since the field is extensively growing in research and development.

  Robust data and proper algorithms to detect patterns are essential for the effective functioning of artificial intelligence systems . This article provides a complete picture of artificial intelligence projects where will start by defining the basics

What is Artificial intelligence?

• The aim of developing intelligent machines is the motive behind artificial intelligence
• It becomes one of the inherent and the most necessary parts of many sectors such as real-time applications (industry 4.0, smart city, and robotics)

Due to its growing importance, the research in artificial intelligence is increasing at large. With the help of our highly specialized and technically well-versed group of experts , you can surely produce the best artificial intelligence research proposal. One of the underlying problems in AI research respect the following characteristics of programming.

• Knowledge and perception
• Learning, planning, and reasoning
• Problem solving
• Manipulation capacity and object motion

Get in contact with us if you want to learn about the strategies used by our specialists to solve artificial intelligence research issues . The important technicalities will then be simply shared with you.

Types of Artificial Intelligence

The following are the most important types of artificial intelligence

• It is developed because of the demand from a large number of users and regulators
• The system is developed in such a way to learn and get trained from critical data sets while protecting the user privacy
• NLP strengths are incorporated or utilized by collaborative AI for superior and advanced manipulation
• Diverse testbed is also it’s characteristic
• This model of artificial intelligence helps in improving its adoption and deployment
• It is built over the trusted brand of Singapore
• Incremental learning of artificial intelligence systems automatically is a characteristic feature of this model
• Artificial general intelligence can in turn be enhanced using this model
• The datasets used in this kind of AI is highly qualitative
• Here nearly a small data set comparable to a small country is utilized

We will do a comparative analysis of all of these topic areas so that you can easily identify the subject study that best meets your demands. We support the wide interpretation of liberty of researchers , which we believe will lead to many of the improvements that society requires.

As a result, we encourage our clients to do in-depth research on any topic on their own and act as a facilitator to them. Then, if necessary, we will examine their thoughts and assist them in selecting the most interesting study topic or writing a artificial intelligence research proposal . Let us now look into the applications of AI below

Artificial Intelligence applications

AI has a lot of applications in diverse fields. It is the technology of the present that has huge potential to become the only technology of the future. In this regard, we have a look into the applications of AI below.

• AI is well known for its use in smart cities and other smart applications like finance, health, transport, and justice delivery

With its capacity to supplement human intelligence and capacities , AI intelligence and human abilities are now getting integrated to produce greater results

• Artificial intelligence is integrated into light detection and ranging systems called LIDAR (combines radar and light) for advanced details during navigation and avoiding Collision

For further explanation on all such technicalities, you can contact us. We can also assist you with any research needs you may very well have. We believe that present techniques should be questioned. This is because we think that only by asking questions can we have a better knowledge of what we’re talking about, and only then can we create optimal technologies .

Latest Research Topics in Artificial Intelligence

The following are some of the important and recent artificial intelligence research topics

• Pattern recognition and expert systems
• Artificial neural networks and natural language processing
• Robotics and genetic algorithm
• Machine learning and computer vision
• Automated reasoning and complex systems
• Intelligent search engine, control, and data mining

We intended to influence society by guiding prospective research at a fair cost in all the above topics . As a result, we supply you with a variety of additional services that you will require during your study. Artificial intelligence Dissertation , as you may know, is made feasible by mathematical operations conducted on digital forms of signals utilizing complex algorithms.

Artificial Intelligence Technologies List

• Deep learning image recognition and computer vision skills are utilised by the vehicles for self-driving
• It can intelligently avoid Collisions and unexpected obstacles and it can also pilot a given vehicle by staying in a particular lane
• Robotics engineering field whose primary aim is to manufacture and develop advanced robots
• Automation is involved in developing autonomous mechanisms and systems
• Machine vision is the technology that allows the computers and other devices to have vision
• Machine learning enables a computer to work on its own without getting programmed for each and every aspect
• By NLP you can process the human words and languages using computers

All these AI-based methods and systems are built on the foundations of coding and mathematics . The programming frameworks and simulation techniques related to AI ought to have been obvious to you. You can also feel free to get in touch with our experts at any time concerning these methodologies. Let us now look into the research proposal format.

Format of a research proposal

The following are the important aspects of a research proposal,

• A suitable and unique title to a topic can attract the reader’s attention
• You need to highlight the important points with regard to the background of the topic and the field development timeline
• Research has to be well establish in depth investigation for providing evidence
• The proposed methods and techniques have to be clearly mentioned along with their merits and demerits of the existing works.
• A detailed working plan along with the timeline has to be developed and your research must be scheduled in line with it
• Sources used in proposal writing must be properly acknowledged in bibliography
• You can also include a reference section in place of bibliography

It is now really important that you have a clear and expert view of the various aspects of artificial intelligence proposal in great detail. Because attempting to write a research proposal by knowing all its necessities in prior can help you get the best outcome. So latest now have a look into every aspect of artificial intelligence research proposal in great detail in the following sections

Conduct Preliminary research

To choose one of the best topics, you need to have preliminary research. Make sure that you look into all the aspects of atopic and choose the most specific issue in place. This helps you to focus your research proposal on the right track. You can get all the books, benchmark references, journals, and authentic websites for collecting information regarding your research objective from us. Make sure to look into both pros and cons of your topic. Consider the following points during preliminary research

• Points that are overlooked by the readers in your research sources
• Potential debatable topics to be addressed
• Your stance over the topic
• Recent breakthroughs in your field

You must include explanations from reliable sources on all these points in your proposal. We provide you with the essential support and motivation to conduct research and complete your artificial intelligence research proposal successfully. We are well versed in the proposal format of all the universities of the world.

To formulate research questions, you can use the phrase ‘I want (or attempted) to know what (why or how) of the problem’ so that it looks standard. Let us now have some more ideas on the topic being selected.

Discovering, narrowing and focusing a researchable topic

• The most interesting topic according to you have to be selected
• Then you need to attempt to write all about the topic in your way
• Have interactions with your peer groups and course instructor
• Finally given up your topic in the form of a question which you should proclaim to address in the proposal

In addition, a topic with potential and reliable reference sources can help you to a greater extent. Here we assist you in fetching advanced research materials and data for any novel topic of your interest. As we have established associations with the world’s top researchers and experts , we can bring any kind of materials for your research at your disposal . Reach out to us for all such most needed research assistance. Let us now look into source selection,

Finding, selecting and reading sources  

As you start looking for the standard sources for your artificial intelligence research proposal we insist you give priority to the following sources

• Standard primary and secondary sources of references
• Limitations, research gaps, and drawbacks of existing methods

You can get the necessary practical explanations along with the massive reliable data from our research guidance facility. With world-class certified developers, writers, and engineers you can get a greater insight into all aspects of computer simulation and artificial intelligence from us. Let us now look into the ways of documenting the collected information.

Grouping, sequencing and documenting information

When you are working to present and document the data collected you must make proper grouping and sequencing of them.

For all formatting and editing guidance, you can check out our website. We are offering one of the best artificial intelligence research proposal writing guidance with highly qualified and experienced writers of the world. We ensure to offer customized online research support 24×7 . Let us now see about writing an outline and a prospectus.

Writing an Outline of Research Proposal

The following are the important questions to be dealt with in your research proposal

• The topic to be dealt
• Significance of the topic
• Relevant background knowledge and material
• Problem statement along with its purpose
• Plan of the organization to support the statement to its best

By ensuring multiple grammatical checks and confidential research support we become the most reputed and trustworthy research guidance providers across all the countries. Also, you can expect complete support from our side concerning assignment writing, paper publication , and survey and conference paper writing , and so on. Let us now talk about writing an introduction,

How to write the introduction section?

The following aspects have to be included with huge importance in your research proposal introduction

• All the important points concerning background and context materials
• Necessary terms and concepts definition
• Proper explanations on the focus and purpose of the research proposal
• Plan of organization has to be revealed perfectly

To better understand the style of writing, you can look into the standard examples of the best and successful research proposals that we guided. We have more than two decades of experience in artificial intelligence research. So our experts are capable of solving all the research issues, problems, and concerns of it . Let us now talk about writing the body of the proposal.

Writing the body

The following are all the important points to be remembered while writing the body of a research proposal

• Develop your proposal in and around your topic
• The sources should not direct your proposal whereas the search of sources have to be in line with your objective
• Integration of the sources and discussion must be given prime importance
• Summarising, analyzing, explaining, and evaluating the published work is more important than making a report of it
• Make sure to include the generalized and specific points about the topic

To include the authentic research data in the body of your artificial intelligence research proposal , readily contact our technical experts. We also provide all necessary help in the successful implementation of accurate codes and writing respective algorithms . Let us now talk about the research proposal conclusion

Writing the conclusion

• In case of complexities in the proposal you are expected to provide a summary
• The importance of findings and observations has to be recorded even before the conclusion part. In cases when such points are missed out, you can add and explain their significance at the end
• In the finishing stage, from being more specific you need to shift towards a generalized approach in line with the introduction
• At last, the scope for further research in the future gives a good frame to your proposal

Get to read the best conclusions from our website. An artificial intelligence research proposal is one of our major services through which we have delivered more than 300+ Artificial Intelligence Projects in the field. With the highly experienced technical team and engineers, we are providing experimentation and Research support to our customers. We will now discuss the important aspects of the experimentation section

Experimental section

• The simulation tools being used have to be introduced and explained properly
• Proper configuration details of the software and hardware are essential
• Description of the data sets have to include their links, attributes, and analysis
• Latest years of papers from authentic sources like Elsevier, Springer, and IEEE
• At least from 50+ papers, doing the literature works
• Clear definition of the performance parameters and metrics can fetch you more credibility
• Graphical and tabular comparative analysis attach the visualization aspect to your study
• Summarization of the result has the potential to retain your study in the mind of the reader

As we mentioned earlier, having a better idea of the simulation tools, techniques, platforms, and software becomes highly significant to conduct the best research. Our experts update themselves regularly to provide advanced technical assistance to you. Let us now see the criteria for writing the best thesis

What are the important criteria for the best proposal?

The best proposal is expected to consist of the solutions and answers to all the following questioning aspects

• Ability to arrive at the result at times of less resolution and quality of data
• Comparatively the ability to solve data processing parameter trade-offs efficiently
• Enhancing accuracy when the training videos and proper guidance is not available to carry out the testing
• Proper explanation for scalability of your system
• Proper statistical information at the introduction with real-time examples
• Unique and many advanced features are expected to be a part of the proposal
• The number and quality of testing features under consideration

For proper technical notes and standard reference sources in order to holistically cover all the above aspects, you can talk to our experts. Let us now have a look into scalability and the aspects of data sets below

• Scalability must handle very large datasets to provide greater accuracy and efficiency
• For this purpose, during testing make sure that you use a large number of servers, users, and devices
• The real-time examples, applications, and innovations have to explain in a easy to comprehend manner
• Evaluating the datasets by comparing only two of them might not be sufficient
• Along with artificial datasets evaluation becomes more standardized
• Dimensionality, noise level, outliers, and data size are the important aspects that can potentially impact your proposal
• Computation of all metrics have to be explained properly
• Number of metrics under consideration were taking up more than six metrics and parameters are recommended

By providing multiple revisions and professional proposal writing guidance they have been rendering excellent expert aid in artificial intelligence research proposal . Zero plagiarism and on-time delivery are our mottos. Get in touch with us to get guidance from the world’s best research experts.

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Artificial Intelligence Research Topics for PhD Manuscripts 2021

Introduction.

Imagine a world where knowledge isn’t limited to humans!!! A world in which computers will think and collaborate with humans to create a more exciting universe. Although this future is still a long way off, Artificial Intelligence has made significant progress in recent years. In almost every area of AI, such as quantum computing, healthcare, autonomous vehicles, the internet of things, robotics, and so on, there is a lot of research going on. So much so that the number of annual Published Research Papers on Artificial Intelligence has increased by 90% since 1996.

Keeping this in mind, there are several sub-topics on which you can concentrate if you want to study and write a thesis on Artificial Intelligence. This article covers a few of these subjects and provides a short overview. Here some of the recent Research Topics ,

• Artificial Intelligence and Machine learning – Recent Trands
• How AI and ML can aid healthcare systems in their response to COVID-19
• Machine learning and artificial intelligence in haematology
• Tackling the risk of stranded electricity assets with machine learning and artificial intelligence

Deep Learning

Deep Learning is a type of machine learning that learns by simulating the internal workings of the human brain in order to process data and make decisions.Deep Learning is a form of machine learning that employs artificial neural networks. These neural networks are linked in a web-like structure, similar to the human brain’s networks (basically a condensed version of our brain!).

Artificial neural networks have a web-like structure that allows them to process data in a nonlinear manner, which is a major advantage over conventional algorithms that can only process data in a linear manner. Rank Brain, one of the variables in the Google Search algorithm, is an example of a deep neural network.

Recent research topics

• Artificial intelligence & deep learning : PET and SPECT imaging
• Hierarchical Deep Learning Neural Network (HiDeNN): A computational science and engineering in AI architecture.
• AI for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using Deep Learning
• Deep learning-enabled medical computer vision

Reinforcement Learning

Reinforcing Learning is an aspect of Artificial Intelligence in which a computer learns something in the same way as humans do. Assume the computer is a student, for example. Over time, the hypothetical student learns from its errors. As a outcome of trial and error, Reinforcement Machine Learning Algorithms learn optimal behaviour.

This means that the algorithm determines the next way to proceed by learning behaviours based on its current state that will increase the reward in the future. This also works for robots, just as it does for humans!

Google’s AlphaGo Computer Programme , for example, used Reinforcement Learning to defeat the world champion in the game of Go (a human!) in 2017.

• Experimental quantum speed-up in reinforcement learning agents
• Potential-based multiobjective reinforcement learning approaches to low-impact agents for AI safety

Robotics is an area concerned with the creation of humanoid robots that can assist humans and perform several acts. In certain cases, robots can behave like humans, but can they think like humans as well?

Kismet, a social interaction robot developed at M.I.T.’s Artificial Intelligence Lab, is an example of this. It understands human body language as well as our voice and responds to them appropriately. Another example is NASA’s Robonaut, which was designed to assist astronauts in space.

• Regulating artificial intelligence and robotics: ethics by design in a digital society
• Regional anaesthesia :usages of artificial intelligence and robotics in
• Third Millennium Life Saving Smart Cyberspace Driven by AI and Robotics

Natural Language Processing

Humans can obviously communicate with each other by speech, but now machines can as well! This is known as Natural Language Processing, and it involves machines analysing and understanding language and expression as it is spoken (which means that if you speak to a computer, it might only respond!).  Speech recognition, natural language production, natural language translation, and other aspects of NLP are all concerned with language. NLP is recently very important in customer service applications, particularly chatbots. These chatbots use machine learning and natural language processing to communicate with users in textual form and respond to their questions. As a result, you get a personal touch in your customer service experiences without actually speaking with a human.

Here are several research papers in the field of Natural Language Processing that have been published. You can look at them to get more ideas for research and thesis topics on this subject.

• Natural Language Processing–Based Virtual Cofacilitator for Online Cancer Support Groups: Protocol for an Algorithm Development and Validation Study
• Sympathetic the temporal evolution of COVID-19 Research Through machine learning and natural language processing

Computer Vision

The internet is full of images! This is the selfie age, and taking and posting a photo has never been easier. Each day, millions of images are uploaded to the internet and viewed. It’s important for computers to be able to see and understand images in order to make the most of the vast amount of images available online. And, while humans can do this without thinking about it, computers find it more difficult! This is where Computer Vision enters the image.

To extract information from images, Computer Vision utilizes Artificial Intelligence. This knowledge may include object detection in the image, image content recognition to group images together, and so on. Navigation for autonomous vehicles using images of the surroundings is one use of computer vision, such as AutoNav, which was used in the Spirit and Opportunity rovers that landed on Mars.

• Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning
• An Open‐Source Computer Vision Tool for Automated Vocal Fold Tracking From Video endoscopy

Recommender Systems

Do you get movie and series recommendations from Netflix based on your previous choices or favourite genres? This is achieved by Recommender Systems, which offer you advice about what to do next from the vast array of options available online. Content-based Recommendation or even Collaborative Filtering may be used in a Recommender System.

The content of all the products is analysed in Content-Based Recommendation. For example, based on Natural Language Processing performed on the books, you might be recommended books that you may enjoy. Collaborative Filtering, on the other hand, analyses your past reading behaviour and then recommends books based on it.

• Artificial intelligence in recommender systems
• Deep Transfer Tensor Decomposition with Orthogonal Constraint for Recommender Systems.
• Recommender systems for configuration knowledge engineering

Internet Of Things

Artificial intelligence is concerned with the creation of systems that can learn to perform human-like tasks based on prior experience and without the need for human interaction. The Internet of Things, on the other hand, is a network of different devices linked to the internet and capable of collecting and exchanging data.

All of these IoT devices now generate a large amount of data, which must be collected and mined in order to produce actionable results. Artificial Intelligence enters the picture at this stage. The Internet of Things is used to collect and manage the massive amounts of data that Artificial Intelligence algorithms need.  As a consequence, these algorithms transform the data into useful actionable results that IoT devices can use.

• Enhanced Medical Systems by using Artificial Intelligence and Internet of Things
• Artificial Intelligence and Internet of Things in Instrumentation and Control in Waste Biodegradation Plants: Recent Developments
• AIoT-Artificial Intelligence of Things

In this blog discussed the recent enhancement for artificial intelligences and their sub field. This will help to the PhD scholar who are interested to research in artificial intelligences domain.

• Shouval, R., Fein, J. A., Savani, B., Mohty, M., & Nagler, A. (2021). Machine learning and artificial intelligence in haematology. British journal of haematology, 192(2), 239-250.
• van der Schaar, M., Alaa, A. M., Floto, A., Gimson, A., Scholtes, S., Wood, A., … & Ercole, A. (2021). How artificial intelligence and machine learning can help healthcare systems respond to COVID-19. Machine Learning, 110(1), 1-14.
• Nyangon, J. (2021). Tackling the risk of stranded electricity assets with machine learning and artificial intelligence. In Sustainable Energy Investment-Technical, Market and Policy Innovations to Address Risk. IntechOpen.
• Saha, S., Gan, Z., Cheng, L., Gao, J., Kafka, O. L., Xie, X., … & Liu, W. K. (2021). Hierarchical Deep Learning Neural Network (HiDeNN): An artificial intelligence (AI) framework for computational science and engineering. Computer Methods in Applied Mechanics and Engineering, 373, 113452.
• Mascagni, P., Vardazaryan, A., Alapatt, D., Urade, T., Emre, T., Fiorillo, C., … & Padoy, N. (2021). Artificial intelligence for surgical safety: automatic assessment of the critical view of safety in laparoscopic cholecystectomy using deep learning. Annals of Surgery.
• Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learning-enabled medical computer vision. npj Digital Medicine, 4(1), 1-9.
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PhD Research Proposal Artificial Intelligence

One of the most important subject areas of computer science is Artificial Intelligence. It provides a wide platform for building a machine with learning capabilities . Artificial intelligence makes machines think and react similarly to humans in uncertain situations. In other words, this machine intelligence to behave artificially like human intelligence is known as artificial intelligence. This page is intended to present you useful information on PhD Research Proposal Artificial Intelligence along with the latest research areas, technologies, challenges, trends, techniques, and ideas !!!

Assume that there is a situation in which a human is performing a particular task by learning and understanding the event to solve associated problems. This human task is performed by machine artificial learning abilities are known as artificial intelligence.

For instance: a self-driving car without a driver. In this, the vehicle monitors the environment and takes effective decisions for secure destination attainment.  

What are the requirements for good research proposal writing? 

We believe that we made you are clear with the exact purpose and importance of artificial intelligence at this moment. Now, we can see about the requirements of good PhD Research Proposal Artificial Intelligence . Basically, the writing of PhD proposal needs more concern and study to create a qualified proposal. Since it is the reflection of your research activities and efforts in the form of valuable words. Here, we have given you a few important tips to prepare good proposal writing.

• Need to be adaptable to access required information and resources
• Need to be meet the expected standard and enhance interest to read
• Need to be original to create a new contribution to the handpicked research area
• Need to be related with your degree and present research areas of artificial intelligence

In general, the PhD research proposal has a standard format to write. As well, it is composed of different components such as title, abstract, introduction, literature study, methodologies, conclusion, and references. In fact, we have a native writer team to give complete assistance in perfect proposal writing. Further, we also help you in literature review writing, paper writing, and thesis writing. Here, we have given you a few important things that need to be focused on while writing PhD Research Proposal Artificial Intelligence.   

What are the Components of a Good Research Proposal? 

• Give a short and crisp title for your research proposal
• Choose a title that addresses your research problem and proposed solutions
• Provide a summary of your research work
• Act as detailed synopsis that answers why, how, and what questions of your research
• Present your selected research area and research problem(s)
• Highlight the significance of your study
• Provide sufficient hypothesis of research
• Mention the methodologies that going to be used as solutions
• Talk about the review of secondary research materials
• Address the identified research gaps in previous related studies
• Do a comparison of techniques and arguments in existing researches
• Describe the contribution and findings of the previous research
• List the merits and demerits of existing research works
• Present system architectural design
• Give a detailed explanation on used research tools and techniques/methodologies
• Speak about the need and importance of choosing those methodologies
• Explain the numerical formulas and used algorithms
• Give justification for your proposed research methodologies
• Mention in what way your research methodologies solve your research problem
• Again give an overview of your research
• Point out the objectives and importance of your research
• Encapsulate all highlights of your research in brief
• A present unique point of your study
• Overall, write nearly two paragraphs
• Provide citation of your referred research websites and books
• Implicitly these references mention your supportive hypothesis
• Narrow down your wide research sources
• Smart picking of research materials will impress the research committee

We hope that you are clear with the fundamentals of writing a good PhD research proposal artificial intelligence . Now, we can see about the three primary research terms of artificial intelligence. Since these terms are most widely used in many research areas of artificial intelligence.  As well, it is categorized into three classifications such as, 

• Exploration Areas
• Real-Time Applications

Our researchers are good at proposing modern research work in upcoming research areas for smart applications . If you are interested to know more research ideas from the following classifications, then make an online or offline connection with us.   

What are three important terminologies in Artificial Intelligence? 

• Genetic Evolutionary
• Logical Rationalism
• Molecular Biological
• Statistical Empiricism
• Neural Connectionism
• Smart System Design
• Learning Approaches
• Inference Mechanism
• Knowledge Representation
• Expert System
• Electronic Commerce
• Bioinformatics
• Intelligent Robots
• Natural Language Processing
• Information Retrieval
• Data Mining

In addition, we have also given you some significant research areas of artificial intelligence . We assure you that all these areas are recognized in current AI research topics and ideas. 

Moreover, we also support you in other important research ideas to support you in all aspects of artificial intelligence . By the by, our first and foremost task in AI research is identifying your interesting research area. Then, we provide you list of the latest research notions and phd topics in artificial intelligence .

Research Areas for PhD Research Proposal Artificial Intelligence

• Reinforcement Learning
• Supervised Learning
• Unsupervised Learning
• Dialogue Systems
• Natural Language
• Understanding
• Recognition
• Classification

Furthermore, we have given you a few important supporting AI technologies. Due to the beneficial impact of AI, it is employed and demanding in several research fields (i.e., other technologies). For your information, here we have given you only a few of them. Once you connect with us, we let you know more about up-to-date research topics of your selected technologies . Specifically, these technologies are currently successful in creating real-time AI applications for the development of a smart society.

Converging Technologies of AI 

• Internet of Things
• Big Data Analytics
• Blockchain Technology
• Lightweight Cryptography
• Cloud Computing
• Software-Defined Networking
• Fog Computing
• 6G Networks
• Industry 4.0
• UAV Communication
• Autonomous Vehicles
• Edge Networks

As a point of fact, AI is treated as the shared technology which used to solve different problems in different technologies. So, it can be recognized in many real-time applications and services. Although this field has so many developments in real-time applications, it has some technical issues that arise in the time of development and deployment . For your reference, here we have listed a few important technical issues of AI in recent research.      

Artificial Intelligence Research Problems 

• Optimized Modern Parameters
• Non-linearity from learning to compensate
• Hard-to-Model Issues
• Knowledge and Learning Representation
• Solution for Computational Infeasibility
• Computationally Understanding Solutions
• Training Policies

Already, we have seen converging technologies of artificial intelligence in an earlier section. To the continuation, now we can see about the current trends of AI. In order to identify these trends, our research team has studied the present and past 2-3 years’ research articles and magazines. Through this review, we analyzed and identified

1) Research gaps that need to address

2) Problems that need enhanced solutions than existing one

From this collection, we have listed only a few of them for your reference. Further, we are also ready to share more trends that are sought by active research scholars in the field of artificial intelligence.    

Artificial Intelligence Current Trends

• Mainly in sustainable developments, energy usage has a key player role
• Provides productive communication plans for improving energy-efficiency
• Support significant services in 6G communication
• Human-sensed data are composed with 5D services to enhance the holographic communication
• Assure high QoS, precision, deterministic in 6G communication
• Need tremendous data rates like Tb/s
• Currently, manufacturing industries are moving towards automation technologies and precision communication
• In this, 6G is assured to give ultra-low delay and ultra-high reliability
• For real-cases, the general data transmission need industrial networks for low latency jitters
• For achieving a secure environ, wireless technologies, IoT and fog-cloud computing are advancing over global sustainability and QoS
• Presently, the 6G network understands 3D communication to enhance several applications like smart transportation, smart cities, smart healthcare, etc.
• For instance – Self-driving vehicles delay < 1ms and reliability > 99.999% for fast decisions over sudden accidents

Now, we can see emerging techniques that play a major role in bringing effective research solutions for different current research problems. As a matter of fact, our developers are proficient-enough to identify the best-fitting research techniques and algorithms for any sort of research problem .

In the case of complications in solving problems, our developers analyze the degree of problem complexity and create hybrid technologies or new algorithms accordingly. Overall, we are good to tackle the problem at any level of complexity in smart ways. Also, we suggest key parameters and development tools that enhance your system performance.   

Latest Techniques in AI 

• Generally, the data are collected from different formats, mode representations and sources
• Merging all these dissimilar data in one place is a tedious task
• For the data fusion, advanced neural networks and bayesian learning is used
• For instance – CNN, RBM, and DBM
• Through sensors, collect raw data and transfer it into high-computational devices for data processing
• This may cause more power usage and high traffic load over the network
• So, it is required to design a system that minimizes load and power usage without losing vital information
• Utilize ANN and perform preprocessing
• Also, network topology and architecture are required to be chosen appropriately for add-on benefits
• Prevent interference for primary user benefits through spectrum sensing
• The significant role of the primary user is to transmit data between secondary users and the succeeding layer
• This process is executed by Cooperative Spectrum Sensing (CSS) with high power usage
• The power usage increases because of report findings and spectrum sensing with respect to a centralized location
• Similarly, Convolutional Neural Network is utilized in Deep Corporate Sensing

Additionally, we have given you some growing ideas about artificial intelligence. These ideas are selected from different trending research areas that gain more attraction from the research community. If you have your own ideas to implement an artificial intelligence project, then we support you to upgrade your idea to match the latest advancements of artificial intelligence. So, create a bond with us, to know new interesting PhD research propsoal artificial intelligence . Overall, we give assistance on not only these ideas but also beyond this list of ideas.   

Emerging Ideas on AI 

• Artificial Intelligence for Internet of Things
• Privacy-Aware AI-assisted Edge System for Trustable Services
• Fast AI Services Migration from Cloud into Edge
• Secure Data Dissemination on AI-assisted Edge Systems
• In-depth Learning Services over Edge Network
• Energy-Aware AI-assisted Edge System for Quality of Services
• Real-time AI-assisted Edge Systems with Optimized Solutions
• Edge-intensive Distributed / Collaborate / Federated Smart Services

On the whole, we are here to update you about the recent research updates of artificial intelligence in every possible area. Particularly, we help you in research problem selection, corresponding solutions selection, PhD Research Proposal Artificial Intelligence Writing, code development, paper writing, paper publication, and thesis writing. So, think smartly and hold your hands with our technical experts to shine your AI research career.

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PhD in Artificial Intelligence

To enter the Doctor of Philosophy in Artificial Intelligence, you must apply online through the UGA  Graduate School web page . There is an application fee, which must be paid at the time the application is submitted.

There are several items which must be included in the application:

• Standardized test scores, including the GRE. 
• 3 letters of recommendation, preferably from university faculty and/or professional supervisors. We encourage you to submit the letters to the graduate school online as you complete the application process.
• A sample of scholarly writing, in English. This can be anything you've written but should give an accurate indication of your writing abilities. The writing sample can be a term paper, research report, journal article, published paper, college paper, etc.
• A completed  Application for Graduate Assistantship , if you are interested in receiving funding. 
• A Statement of Purpose.
• A Resume or Curriculum Vitae.

Further information on program admissions is found in the AI Institute Frequently Asked Questions (FAQ) . 

International Students should also review the links on the  Information for International Students  page for additional information relevant to the application process.

Graduate School Policies

University of Georgia Graduate School policies and requirements apply in addition to (and, in cases of conflict, take precedence over) those described here. It is essential that graduate students familiarize themselves with Graduate School policies, including minimum and continuous enrollment  and other policies contained in the Graduate School Bulletin.

Students should also familiarize themselves with Graduate School Dates and Deadlines relevant to the degree.

Degree Requirements

Students of the doctoral program must complete a minimum of 40 hours of graduate coursework and 6 hours of dissertation credit (for a total of 46 credit hours), pass a comprehensive examination, and write and defend a dissertation. In addition, the University requires that all first-year graduate students enroll in a 1-credit-hour GradFirst seminar . Each of these requirements is described in greater detail below.

The degree program is offered using an in-person format, and classes are in general scheduled for full-time students. There are currently no special provisions for part-time, online, or off-campus students. Students are expected to attend all meetings of classes for which they are registered.

Program of Study

The Program of Study must include a minimum of 40 hours of graduate course work and a minimum of 6 hours of dissertation credit. Of the 40 hours of graduate course work, at least 20 hours must be 8000-level or 9000-level hours.

Required Courses

The following courses must be completed unless specifically waived for students entering the program with a master’s degree in Artificial Intelligence or a related field, or for students with substantially related graduate course work. All waived credits may be replaced by an equal number of doctoral research or doctoral dissertation credits (ARTI 9000, Doctoral Research or ARTI 9300, Doctoral Dissertation). Substitutions must be approved for a particular student by that student's Advisory Committee and by the Graduate Coordinator.

• PHIL/LING 6510  Deductive Systems (3 hours)
• CSCI 6380  Data Mining (4 hours) or CSCI 8950  Machine Learning (4 hours)
• CSCI/PHIL 6550  Artificial Intelligence (3 hours)
• ARTI 6950  Faculty Research Seminar (1 hour)
• ARTI/PHIL 6340 Ethics and Artificial Intelligence (3 hours)

Elective Courses

In addition to the required courses above, at least 6 additional courses must be taken from Groups A and Group B below, subject to the following requirements. 

• At least 2 courses must be taken from Group A, from at least 2 areas.
• At least 2 courses must be taken from Group B, from at least 2 areas.
• At least 3 courses must be taken from a single area comprising the student’s chosen area of emphasis .

Since not all courses have the same number of credit hours, Ph.D. students may need to take additional graduate courses to complete the 40 hours.

AREA 1: Artificial Intelligence Methodologies

• CSCI 6560  Evolutionary Computing (4 hours)
• CSCI 8050  Knowledge Based Systems (4 hours)
• CSCI/PHIL 8650  Logic and Logic Programming (4 hours)
• CSCI 8920  Decision Making Under Uncertainty (4 hours)
• CSCI/ENGR 8940  Computational Intelligence (4 hours)
• CSCI/ARTI 8950  Machine Learning (4 hours)

AREA 2: Machine Learning and Data Science

• CSCI 6360  Data Science II (4 hours)
• CSCI 8360  Data Science Practicum (4 hours)
• CSCI 8945  Advanced Representation Learning (4 hours)
• CSCI 8955  Advanced Data Analytics (4 hours)
• CSCI 8960  Privacy-Preserving Data Analysis (4 hours)

AREA 3: Machine Vision and Robotics

• CSCI/ARTI 6530  Introduction to Robotics (4 hours)
• CSCI 6800  Human Computer Interaction (4 hours)
• CSCI 6850  Biomedical Image Analysis (4 hours)
• CSCI 8850  Advanced Biomedical Image Analysis (4 hours)
• CSCI 8820  Computer Vision and Pattern Recognition (4 hours)
• CSCI 8530  Advanced Topics in Robotics (4 hours)
• CSCI 8535  Multi Robot Systems (4 hours)

AREA 4: Cognitive Modeling and Logic

• PHIL/LING 6300  Philosophy of Language (3 hours)
• PHIL 6310  Philosophy of Mind (3 hours)
• PHIL/LING 6520  Model Theory (3 hours)
• PHIL 8310  Seminar in Philosophy of Mind (max of 3 hours)
• PHIL 8500  Seminar in Problems of Logic (max of 3 hours)
• PHIL 8600  Seminar in Metaphysics (max of 3 hours)
• PHIL 8610  Epistemology (max of 3 hours)
• PSYC 6100  Cognitive Psychology (3 hours)
• PSYC 8240  Judgment and Decision Making (3 hours)
• CSCI 6860  Computational Neuroscience (4 hours)

AREA 5: Language and Computation

• ENGL 6885  Introduction to Humanities Computing (3 hours)
• LING 6021  Phonetics and Phonology (3 hours)
• LING 6080  Language and Complex Systems (3 hours)
• LING 6570  Natural Language Processing (3 hours)
• LING 8150  Generative Syntax (3 hours)
• LING 8580  Seminar in Computational Linguistics (3 hours)

AREA 6: Artificial Intelligence Applications

• ELEE 6280  Introduction to Robotics Engineering (3 hours)
• ENGL 6826  Style: Language, Genre, Cognition (3 hours)
• ENGL/LING 6885  Introduction to Humanities Computing (3 hours)
• FORS 8450  Advanced Forest Planning and Harvest Scheduling (3 hours)
• INFO 8000  Foundations of Informatics for Research and Practice
• MIST 7770  Business Intelligence (3 hours)

Students may under special circumstances use up to 6 hours from the following list to apply towards the Electives group requirement. 

• ARTI 8800  Directed Readings in Artificial Intelligence
• ARTI 8000  Topics in Artificial Intelligence

Other courses may be substituted for those on the Electives lists, provided the subject matter of the course is sufficiently related to artificial intelligence and consistent with the educational objectives of the Ph.D. degree program. Substitutions can be made only with the permission of the student's Advisory Committee and the Graduate Coordinator.

In addition to the specific PhD program requirements, all first-year UGA graduate students must enroll in a 1 credit-hour GRSC 7001 (GradFIRST) seminar which provides foundational training in research, scholarship, and professional development. Students may enroll in a section offered by any department, but it is recommended that they enroll in a section offered by AI Faculty Fellows for AI students. More information is available at the  Graduate School website .

Core Competency

Core competency must be exhibited by each student and certified by the student’s advisory committee. This takes the form of achievement in the required courses of the curriculum. Students entering the Ph.D. program with a previous graduate degree sufficient to cover this basic knowledge will need to work with their advisory committee to certify their core competency. Students entering the Ph.D. program without sufficient graduate background to certify core competency must take at least three of the required courses, and then pursue certification with their advisory committee. A grade average of at least 3.56 (e.g., A-, A-, B+) must be achieved for three required courses (excluding ARTI 6950). Students below this average may take the fourth required course and achieve a grade average of at least 3.32 (e.g., A-, B+, B+, B).

Core competency is certified by the unanimous approval of the student's Advisory Committee as well as the approval by the Graduate Coordinator. Students are strongly encouraged to meet the core competency requirement within their first three enrolled academic semesters (excluding summer semester).  Core Competency Certification must be completed before approval of the Final Program of Study.

Comprehensive Examination

Each student of the doctoral program must pass a Ph.D. Comprehensive Examination covering the student's advanced coursework. The examination consists of a written part and an oral part. Students have at most two attempts to pass the written part. The oral part may not be attempted unless the written part has been passed.

Admission to Candidacy

The student is responsible for initiating an application for admission to candidacy once all requirements, except the dissertation prospectus and the dissertation, have been completed.

Dissertation and Dissertation Credit Hours

In addition to the coursework and comprehensive examination, every student must conduct research in artificial intelligence under the direction of an advisory committee and report the results of his or her research in a dissertation acceptable to the Graduate School. The dissertation must represent originality in research, independent thinking, scholarly ability, and technical mastery of a field of study. The dissertation must also demonstrate competent style and organization. While working on his/her dissertation, the student must enroll for a minimum of 6 credit hours of ARTI 9300 Doctoral Dissertation spread over at least 2 semesters.

Advisory Committee

Before the end of the third semester, each student admitted into the program should approach relevant faculty members and form an advisory committee. Until the committee is formed, the student will be advised by the graduate coordinator. The committee consists of a major professor and two other faculty members, as follows:

• The major professor and at least one other member must be full members of the Graduate Program Faculty.
• The major professor and at least one other member must be Institute for Artificial Intelligence Faculty Fellows.

Deviations from the 3-member advisory committee structure, including having more members, are in some cases permitted but must conform to Graduate School policies. 

The major professor and advisory committee shall guide the student in planning the dissertation.  The committee shall agree upon, document, and communicate expectations for the dissertation. These expectations may include publication or submission requirements, but, should not exceed reasonable expectations for the given research domain. During the planning stage, the student will prepare a dissertation prospectus in the form of a detailed written dissertation proposal. It should clearly define the problem to be addressed, critique the current state-of-the-art, and explain the contributions to research expected by the dissertation work. When the major professor certifies that the dissertation prospectus is satisfactory, it must be formally considered by the advisory committee in a meeting with the student. This formal consideration may not take the place of the comprehensive oral examination.

Approval of the dissertation prospectus signifies that members of the advisory committee believe that it proposes a satisfactory research study. Approval of the prospectus requires the agreement of the advisory committee with no more than one dissenting vote as evidenced by their signing an appropriate form to be filed with the graduate coordinator’s office.  

Graduation Requirements - Forms and Timeline

Before the end of the third semester in residence, a student must begin submitting to the Graduate School, through the graduate coordinator, the following forms: (i) a Preliminary Program of Study Form and (ii) an Advisory Committee Form. The Program of Study Form indicates how and when degree requirements will be met and must be formulated in consultation with the student's major professor. An Application for Graduation Form must also be submitted directly to the Graduate School. Forms and Timing must be submitted as follows:

• Advisory Committee Form (G130)—end of third semester
• Core Competency Form (Internal to IAI)—beginning of fourth semester
• Preliminary Doctoral Program of Study Form—Fourth semester
• Final Program of Study Form (G138)—before Comprehensive Examination
• Application for Admission to Candidacy (G162)—after Comprehensive Examination
• Application for Graduation Form (on Athena)—beginning of last semester
• Approval Form for Doctoral Dissertation (G164)—last semester
• ETD Submission Approval Form (G129)—last semester

Students should frequently check the Graduate School Dates and Deadlines webpage to ensure that all necessary forms are completed in a timely manner.

Student Handbook

Additional information on degree requirements and AI Institute policies can be found in the AI Student Handbook .

For information regarding the graduate programs in IAI, please contact: 

Evette Dunbar [email protected] Boyd GSRC, Room 516 706-542-0358

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Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD

London, Bloomsbury

Artificial Intelligence (AI) has the potential to transform health and healthcare systems globally, yet few individuals have the required skills and training. To address this challenge, our Centre For Doctoral Training (CDT) in AI-Enabled Healthcare Systems will create a unique interdisciplinary environment to train the brightest and best healthcare artificial intelligence scientists and innovators of the future.

UK tuition fees (2024/25)

Overseas tuition fees (2024/25), programme starts, applications accepted.

Applications closed

The Centre for Doctoral Training recruits in at least two rounds. Applicants are advised to apply early, priority will be given to those who have applied in round one.

• Entry requirements

A minimum of an upper second class honours undergraduate degree, or a Master's degree in a relevant discipline (or equivalent international qualifications or experience). Our preferred subject areas are Physical Sciences (Computer Science, Engineering, Mathematics and Physics) or Clinical / Biomedical Science. Applicants with a clinical background or degree in Biomedical Science must be able to demonstrate strong computational skills. You must be able to demonstrate an interest in creating, developing or evaluating AI-enabled Healthcare systems.

The English language level for this programme is: Level 2

UCL Pre-Master's and Pre-sessional English courses are for international students who are aiming to study for a postgraduate degree at UCL. The courses will develop your academic English and academic skills required to succeed at postgraduate level.

Further information can be found on our English language requirements page.

If you are intending to apply for a time-limited visa to complete your UCL studies (e.g., Student visa, Skilled worker visa, PBS dependant visa etc.) you may be required to obtain ATAS clearance . This will be confirmed to you if you obtain an offer of a place. Please note that ATAS processing times can take up to six months, so we recommend you consider these timelines when submitting your application to UCL.

Equivalent qualifications

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website .

International applicants can find out the equivalent qualification for their country by selecting from the list below. Please note that the equivalency will correspond to the broad UK degree classification stated on this page (e.g. upper second-class). Where a specific overall percentage is required in the UK qualification, the international equivalency will be higher than that stated below. Please contact Graduate Admissions should you require further advice.

About this degree

Every student who is accepted onto the AI-enabled Healthcare Systems Centre for Doctoral Training (CDT) must take the MRes Artificial Intelligence Enabled Healthcare in their first year. This will be followed by a 3 year PhD. Throughout this period the CDT will continue to closely monitor the need for continuing training and support, tailored to each student, and provide ongoing training in research skills. The MRes is not currently available as a stand-alone programme.

The MRes programme covers the core competencies of artificial intelligence and has a central emphasis on how healthcare organisations work. Ethical training for medical artificial intelligence will be explicitly emphasised alongside a broader approach to responsible research, innovation and entrepreneurship.

During the MRes year, students will learn the statistical underpinnings of machine learning theory, get a practical grounding in research software engineering and the principles of healthcare and medical research, as well as a thorough treatment of topics in machine learning, advanced statistics and principles of data science.

As part of the MRes, alongside the core and elective modules, you will complete a substantial Masters-level project of your choice, working with a supervisory team that will normally include a clinician and an academic. The project you work on during your MRes normally leads to the chosen PhD research topic.

The remaining years will be more like a traditional PhD, which leads to the presentation of a PhD thesis at the end of the fourth year. During your PhD you will remain involved in CDT activities and will continue to work closely with relevant health professionals and clinical teams through our NHS partners and leading academics at UCL.

As a cohort based PhD programme, students will also have the opportunity to participate in a range of seminars, training programmes, placements and other activities, including UCL's Doctoral Skills Development Programme.

Training Opportunities The CDT programme consists of a range of activities and events including:

• A Mini-MD programme where trainees undertake an immersive clinical experience within an NHS setting
• Annual CDT Conference
• Seminar series
• PPI Training
• Responsible Research & Innovation
• Communication Skills
• Entrepreneurship
• Ethical Training
• The opportunity to attend training programmes offered by the Alan Turing Institute
• Opportunities for internships and placements with industry partners

More information can be found on the CDT Website .

Who this course is for

The Centre for Doctoral Training programme is for students with an interest in creating and developing AI solutions aimed to transform and solve healthcare challenges. The CDT programme is embedded within a NHS setting, and should appeal to students keen to develop clinical knowledge and algorithmic/ programming expertise.

What this course will give you

• Benefit from UCL's excellence both in computational science and biomedical research innovating in AI;
• Be supervised by world-leading clinicians and AI researchers in areas related to your research;
• Work within a real-world setting, embedded within hospitals, allowing you to gain a practical understanding of the value and limitations of the datasets and the translational skills required to put systems into practice;
• Have the opportunity to not only apply AI to healthcare but to apply healthcare to AI, generating novel large-scale open datasets driving methodological innovation in AI;
• Become a future leader in solving the most pressing healthcare challenges with the most innovative AI solutions;
• Study at UCL, which is rated No.1 for research power and impact in medicine, health and life sciences (REF 2021) and 9th in the world as a university (QS World Rankings 2024).

The foundation of your career

We do not yet have any graduates from the four-year programme, our first cohort of students will be graduating over the next few months. We expect them to stay within the field of AI and healthcare, and much like previous graduates from our experienced CDT supervisors, they will go onto successful careers in academia and industry. 

Employability

The distinctive characteristics of our programme allow us to produce graduates who are prepared to:

• engineer adaptive and responsive solutions that use AI to deal with complexity;
• innovate across all levels of care, from community services to specialist hospitals;
• be comfortable working with patients and professionals, and responding to their input;
• appreciate the importance of addressing health needs rather than creating new demand.

The Institute's research departments collaborate with third-sector and governmental organisations, as well as members of the media, both nationally and internationally to ensure the highest possible impact of their work beyond the academic community. Students are encouraged to do internships with relevant organisations where funding permits. Members of staff also collaborate closely with academics from leading institutions globally.

Teaching and learning

Various teaching and learning methods are employed to facilitate effective learning and cater to different learning styles. Below are some common types of teaching methods that may be used across the programme:

Interdisciplinary Teaching: Interdisciplinary teaching involves integrating knowledge and skills from multiple disciplines or subject areas to provide a comprehensive understanding of a topic, particularly AI and healthcare. This approach encourages students to make connections between different subjects and fosters critical thinking and problem-solving abilities.

Lecture-Based Teaching: Lecture-based teaching is a traditional method where the instructor presents information to students through spoken words. It involves the teacher sharing knowledge, concepts, and theories, while students take notes and listen actively. This method is effective for conveying large amounts of information and providing foundational knowledge.

Practical Coding Sessions: Practical coding sessions are hands-on learning experiences where students actively engage in coding exercises, programming tasks, and problem-solving activities. These sessions are essential for AI and programming-related subjects (machine learning, etc) as they allow students to apply theoretical knowledge to real-world scenarios.

Interactive Teaching: Interactive teaching methods encourage active participation and engagement from students. These methods can include discussions, debates, group activities, and case studies, in particular in several modules such as Journal Club. Interactive teaching fosters collaboration, communication skills, and a deeper understanding of the subject matter.

Project-Based Learning: Project-Based Learning involves assigning students long-term projects that require them to investigate and address real-world problems or challenges (such as AI & healthcare group project). It enhances critical thinking, research skills, and creativity while promoting independent learning and teamwork.

Collaborative Learning: Collaborative learning involves students working together in small groups or pairs to solve problems, discuss ideas, and complete tasks. This method promotes teamwork, communication, and the exchange of diverse perspectives.

The use of these teaching/learning methods can vary depending on the subject matter, the goals of the programme, and the preferences of the instructors in the MRes year. Our educational programme incorporates a mix of these methods to cater to the diverse needs of learners and create a well-rounded learning experience.  

Compulsory Modules:

CHME0033 Dissertation in Artificial Intelligence Enabled Healthcare

CHME0032 Healthcare Artificial Intelligence Journal Club

Optional Modules

CHME0012 Principles of Health Data Science

CHME0013 Data Methods for Health Research

CHME0015 Advanced Statistics for Records Research

CHME0016 Machine Learning in Healthcare and Biomedicine

CHME0031 Programming with Python for Health Research

CHME0034 Computational Genetics of Healthcare

CHME0035 Advanced Machine Learning for Healthcare

CHME0039 Artificial Intelligence in Healthcare Group Project

COMP0084 Information Retrieval and Data Mining

Please note that the list of modules given here is indicative. This information is published a long time in advance of enrolment and module content and availability is subject to change.

Assessment methods are crucial components of an educational programme, as they evaluate students' understanding, knowledge, skills, and application of concepts. Here are various types of assessment methods that may be used across the programme:

Exams: Traditional exams are a common assessment method that tests students' knowledge and understanding of the course material. These exams typically involve a time-bound written assessment, where students respond to questions related to the subject matter.

Open-Book Exam: In an open-book exam, students are allowed to refer to their textbooks, notes, or other resources during the assessment. The questions in these exams are often designed to test higher-order thinking and problem-solving abilities, as students have access to reference materials.

Coursework: Coursework assessments involve various assignments, essays, reports, or projects that students complete throughout the course. These assessments may cover specific topics or practical applications and help to assess students' comprehension and critical thinking skills.

Coding Exam: A coding exam is specifically designed for courses related to computer science, software development, or programming. Students are given coding challenges or programming tasks that assess their coding proficiency and problem-solving abilities.

Collaborative Project: In a collaborative project assessment, students work in groups to tackle a complex problem or complete a substantial task. This assessment measures teamwork, communication, time management, and the ability to achieve shared goals.

Presentation and Q&A: Presentations require students to deliver a talk on a given topic or project. The presentation assesses their ability to communicate effectively, organize information, and present ideas coherently. Often, a question and answer (Q&A) session follows the presentation to delve deeper into the topic.

Research Proposal: A research proposal is a preliminary plan for a research project that students submit to demonstrate their research capabilities. It outlines the research question, objectives, methodology, and potential outcomes of the study.

Dissertation Writing: Dissertation writing is typically reserved for higher education levels, such as undergraduate and postgraduate studies. It involves an extended research project on a specific subject, allowing students to demonstrate research, analytical, and academic writing skills.

Online Quizzes and Tests: Online quizzes and tests are digital assessments that may be used for formative or summative purposes. They are often employed in blended or online learning environments.

The use of assessment methods will vary based on the nature of the programme, the subject matter throughout the MRes year. A well-balanced combination of assessment types ensures that students' diverse abilities and learning styles are appropriately evaluated while providing a comprehensive understanding of their progress and achievements.

During the MRes 4 hours of a student's time is spent in tutorials per week and/or, 6-8 hours in lectures per week, and a further 20-24 hours in independent study per week.

Research areas and structure

• AI-enabled diagnostics or prognostics
• AI-enabled operations
• AI-enabled therapeutics
• Public Health Data Science
• Machine Learning in Health Care
• Public Health informatics
• Learning health systems
• Electronic health records and clinical knowledge management
• e-health and m-health
• Clinical Decision Support Systems

Research environment

Our research environment offers a unique degree programme that stands out among competitors. We provide students with the exceptional opportunity to explore the cutting-edge intersection of AI technology and healthcare applications. Our curriculum emphasizes research and innovation skills, empowering students to become independent researchers and adept problem solvers. A key difference is our close collaboration with clinicians and front-line practitioners. This interaction fosters a holistic understanding of healthcare challenges and real-world applications, ensuring that our graduates are equipped with practical knowledge and solutions. Our programme is inclusive, welcoming students from both computational and clinical backgrounds, creating a diverse and dynamic learning environment.

Students studying the programme full-time will be expected to complete 180 credits during the academic year. 

Students studying the programme part-time will be expected to complete 180 credits across two academic years. 

Accessibility

Details of the accessibility of UCL buildings can be obtained from AccessAble accessable.co.uk . Further information can also be obtained from the UCL Student Support and Wellbeing team .

Fees and funding

Fees for this course.

The tuition fees shown are for the year indicated above. Fees for subsequent years may increase or otherwise vary. Where the programme is offered on a flexible/modular basis, fees are charged pro-rata to the appropriate full-time Master's fee taken in an academic session. Further information on fee status, fee increases and the fee schedule can be viewed on the UCL Students website: ucl.ac.uk/students/fees .

Additional costs

All studentships include a research training support grant, which covers additional research costs throughout students' time on the programme.

For more information on additional costs for prospective students please go to our estimated cost of essential expenditure at Accommodation and living costs .

Funding your studies

Please visit the CDT website for funding information.

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarships and Funding website .

Note for applicants: When applying on UCL Select, please select MRes Artificial Intelligence enabled healthcare to apply for programme.

Please note that you may submit applications for a maximum of two graduate programmes (or one application for the Law LLM) in any application cycle.

Got questions? Get in touch

Institute of Health Informatics

[email protected]

UCL is regulated by the Office for Students .

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• Corpus ID: 209436745

PHD PROPOSAL IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

• Published 2019
• Computer Science

25 References

Large-scale machine learning and applications, a survey on transfer learning, distributed gaussian processes, deterministic execution on gpu architectures, exact gaussian processes on a million data points, a loewner-based approach for the approximation of engagement-related neurophysiological features, the worst-case execution-time problem—overview of methods and survey of tools, electrocardiogram generation with a bidirectional lstm-cnn generative adversarial network, pgans: personalized generative adversarial networks for ecg synthesis to improve patient-specific deep ecg classification, supplementary for: deep learning with convolutional neural networks for eeg decoding and visualization, related papers.

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PhD on artificial intelligence for renewable energy and sustainability

Fully-funded PhD in the area of artificial intelligence for renewable energy and sustainability.

Application deadline

Funding information.

A stipend of £19,000 for 22/23, which will increase each year in line with the UK Research and Innovation (UKRI) rate, plus Home rate fee allowance of £4,596 (with automatic increase to UKRI rate each year). The studentship is offered for 3.5 years. For exceptional international candidates, there is the possibility of obtaining a scholarship to cover overseas fees.

Supervised by

Dr Erick Sperandio Nascimento

Prof Prashant Kumar

Renewable energy sources have gained increased attention and investments from the industries, governments and society, such as wind, solar, and hydrological sources, to enable a more sustainable and yet economically feasible development. However, the building and operationalization of renewable power plants face a series of challenges that must be tackled in order to improve their adoption. One of the main challenges resides in the ability to accurately predict the meteorological parameters that influence the generation of wind and solar energy from shorter to longer term, which becomes even more challenging in the face of climate change.

Therefore, this project aims at researching, developing and building AI-based solutions that can support the development of more reliable and accurate weather forecasting systems applied to the prediction of solar and wind energy generation, extreme weather events forecasting and their effects, air quality and sustainability. Historical data from publicly available sources will be used, like surface weather stations, GDAS/ECMWF/Era5 and satellite data, among others, along with information about wind turbines and photovoltaic cells.

We seek for exceptional candidates that are willing to develop AI-based clean air solutions by researching and building cutting-edge approaches and techniques in the fields of deep learning, physics-informed and graph neural networks, spatial-temporal modelling, model explainability and interpretability, time series foundation models, physical modelling and data-driven approaches, among others, applied to the challenges related to the fields of renewable energies and sustainability.

The applicant will be directly involved with research activities in the Global Centre for Clean Air Research (GCARE) and the People-Centred AI Institute, both in the University of Surrey, having access to an amazing set of resources, infrastructure and people engaged to deliver world-class researches and technologies with a focus on the well-being of people and on the scientific and technological development of the academia, industry and society.

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Eligibility criteria.

This studentship is open to UK and international candidates.

All applicants should have (or expect to obtain) a first-class degree in a numerate discipline (mathematics, science or engineering) or MSc with distinction (or 70% average) and a strong interest in pursuing research in this field.

Additional experience which is relevant to the area of research is also advantageous.

English language requirements

IELTS minimum 6.5 overall with 6.0 in writing, or equivalent.

How to apply

Applications should be submitted via the PhD Vision, Speech and Signal Processing programme .

In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor.

Studentship FAQs

Read our  studentship FAQs  to find out more about applying and funding.

03 March 2023

Contact details

Erick giovani sperandio nascimento.

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Artificial Intelligence and Data Analytics (AIDA) Group

Featured story.

Suggested PhD Projects

Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. All applicants are invited to contact the academic associated with the project when making an application.

Machine Learning for the Pharmacology of Ageing

Contact:  alex freitas.

Recently, there has been a growing interest in ageing research, since the proportion of elderly people in the world’s population is expected to increase substantially in the next few decades. As people live longer, it becomes increasingly more common for a person to suffer from multiple age-related diseases. Since old age is the ultimate cause or the greatest risk factor for most of these diseases, progress in ageing research has the potential to lead to a more cost-effective treatment of many age-related diseases in a holistic fashion. In this context, researchers have collected a significant amount of data about ageing-related genes and medical drugs affecting an organism’s longevity – mainly about simpler model organisms, rather than humans. This data is often freely available on the web, which has facilitated the application of machine learning methods to the pharmacology or biomedicine of ageing, to try to discover some knowledge or patterns in such datasets. This project will focus on developing machine learning algorithms for analysing data about the pharmacology of ageing, i.e., data about medical drugs or chemical compounds that can be used as an intervention against ageing, mainly in model organisms. The broad type of machine learning method to be developed will be supervised machine learning (mainly classification), but the specific type of algorithm to be developed will be decided later, depending on the student’s interest and suitability to the target datasets. Note that, although this is an interdisciplinary project, this is a project for a PhD in Computer Science, so the student will be expected to develop a novel machine learning method. As examples of interdisciplinary papers on machine learning for ageing research, see e.g. (the first paper is particularly relevant for this project, whilst the second includes a broader discussion about machine learning for ageing research):

Relevant References:

D.G. Barardo, D. Newby, D. Thornton, T. Ghafourian, J.P. de Magalhaes and A.A. Freitas. Machine learning for predicting lifespan-extending chemical compounds. Aging (Albany NY), 9(7), 1721-1737, 2017.

Fabris, J.P. de Magalhaes, A.A. Freitas. A review of supervised machine learning applied to ageing research. Biogerontology, 18(2), 171-188, April 2017.

Machine Learning with Fairness-Aware Classification Algorithms

This project involves the classification task of machine learning, where an algorithm has to predict the class of an object (e.g. a customer or a patient) based on properties of that object (e.g. characteristics of a customer or patient). There are now many types of classification algorithms, and in general these algorithms were designed with the only (or main) goal of maximizing predictive performance. As a result, the application of such algorithms to real-world data about people often leads to predictions which have a good predictive accuracy but are unfair, in the sense of discriminating (being biased) against certain groups or types of people – characterized e.g. by values of attributes like gender or ethnicity. In the last few years, however, there has been a considerable amount of research on fairness-aware classification algorithms, which take into account the trade-off between achieving a high predictive accuracy and a high degree of fairness. The project will develop new classification algorithms to cope with this trade-off, focusing on classification algorithms that produce interpretable predictive models, rather than black box models.

[1] Friedler, A.A., Scheidegger, C., Venkatasubramanian, S., Choudhary, S., Hamilton, E.P. and Roth, D. A comparative study of fairness-enhancing interventions in machine learning. Proc. 2nd ACM Conf. on Fairness, Accountability and Transparency (FAT’19), 329-338. ACM Press, 2019.

[2] Mehrabi, N., Morstatter, F., Saxena, N., Lerman, K., & Galstyan, A. A survey on bias and fairness in machine learning. arXiv preprint: arXiv:1908.09635. 2019.

Cognition-enabled lifelong robot learning of behavioural and linguistic experience

Contact:  ioanna giorgi.

Present-day cognitive robotics models draw on a hypothesised developmental paradigm of human cognitive functions to devise low-order skills in robots, such as perception, manipulation, navigation and motor coordination. These methods exploit embodied and situated cognition theories that are rooted in motor behaviour and the environment. In other words, the body of a physical artefact (e.g., a robot) and its interactions with the environment and other organisms in it contribute to the robot’s cognition. However, it is not clear how these models can explain or scale up to the high-level cognitive competence observed in human behaviour (e.g., reasoning, categorisation, abstraction and voluntary control). One approach to model robot learning of behavioural and cognitive skills is in incremental and developmental stages that resemble child development. According to child psychology and behaviour, conceptual development starts from perceptual clustering (e.g., prelinguistic infants grouping objects by colour) and progresses to nontrivial abstract thinking, which requires a fair amount of language . Thus, to solve the problem of modelling high-level cognitive skills in robots, language, in interaction with the robot’s body, becomes inseparable from cognition. This project is aimed at following a cognitive and developmental approach to robot learning that will allow robots to acquire behavioural and linguistic skills at a high level of cognitive competence and adaptation as humans. This learning should be lifelong : humans apply earlier-learned skills to make sense of continuous novel stimuli, which allows them to develop, grow and adjust to more complex practices. One such cognitive robot can be used across various themes: human-robot interaction using theory of mind (ToM) skills for robots, social robots and joint human-robot collaboration.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and gadgets like AR Epson glasses and Microsoft HoloLens.

Attention model for agent social learning during human-robot interaction.

Successful human-robot interaction requires that robots learn by observing and imitating human behaviour. The theory of learning behaviour through observation is referred to as social learning . Behavioural learning can also be enhanced by the environment itself and through reinforcement (i.e., establishing and encouraging a pattern of behaviour). One important component of such learning is cognitive attention , which deals with the degree to which we notice a behaviour. Cognitive attention renders some inputs more relevant while diminishing others, with the motivation that more focus is needed for the important stimuli in the context of social learning. Attention brings forth positive reinforcement (reward) or negative reinforcement (punishment). If the reward is greater than the punishment, behaviour is more likely to be imitated and reciprocated. In human-robot interaction, attention is crucial for two reasons: 1) to respond or reciprocate the behaviour appropriately during the interaction, and 2) to learn or imitate that behaviour for contingencies. This project is aimed at devising a cognitive attention model of a robot for social learning. The model will include memory, reasoning, language and multi-sensory data processing, i.e., “natural” stimuli during the interaction such as from vision, speech and sensorimotor experience. It can be based on a cognitive architecture approach or alternative computational approaches. The solution should ideally be encompassing multiple aspects of interaction (verbal and non-verbal), but it can also focus on such specific aspects (e.g., visual attention or intention reading).

How can a robot learn skills from a human tutor

Contact:  giovanni masala.

The aim of this project is to enhance robot learning from a human tutor, similar to a child who learns from a human teacher. The agent will develop the ability to communicate through natural language from scratch, by interacting with a tutor, recognising their verbal and non-verbal inputs as well as emotions, and, finally, grounding the word meaning in the external environment. The project will start from an existing neuro-cognitive architecture under development [1], based on a Human-like approach to learning, progressively incrementing knowledge and language capabilities through experience and ample exposure, using a corpus based on early language lexicons (preschool literature). The robot will integrate with visuospatial information-processing mechanisms for embodied language acquisition, exploiting affective mechanisms of emotion detection for learning and cognition. The agent will be embodied into a humanoid robot as opposed to a computer or a virtual assistant, to enable real-world interactions with the humans and the external environment, to learn and refine its natural language understanding abilities guided or depending on the teacher’s emotions and visual input (object associations with the words, facial expression, and gestures). Emotions will influence the cognitive attention of the robotic agent, modulating the selectivity of attention on specific tasks, words, and objects, and motivating actions and behaviour.

[1] Golosio B, Cangelosi A, Gamotina O, MASALA GL, A Cognitive Neural Architecture Able to Learn and Communicate through Natural Language. PLoS ONE 10(11): e0140866, 2015.

Note: The Cognitive Robotics and Autonomous Systems (CoRAS) laboratory at the School of Computing has access to several humanoids (NAO) and socially interactive robot platforms (Buddy Pro, Q.BO One, Amy A1), mobile robots (Turtlebot Waffle Pi, Burger), pet-like companion robots and devices like AR Epson glasses and Microsoft HoloLens.

Explainability and Interpretability of Machine/Deep learning techniques in medical imaging

In medicine is very important the acceptance of Machine Learning systems not only in terms of performance but also considering the degree to which a human can understand the cause of a decision. Nowadays, the application of Computer Aided Detection Systems in radiology is often based on Deep Learning Systems thanks to their high performance. In general, more accurate models are less explainable  and there is a scientific interest in the field of Explainable Artificial Intelligence, to develop new methods that explain and interpret ML models. There is not a concrete mathematical definition for interpretability or explainability, nor have they been measured by some metric; however, a number of attempts have been made in order to clarify not only these two terms but also related concepts such as comprehensibility. A possible target (but other medical diseases are allowed) of this research is a model to discover the severity of Breast Arterial Calcifications. Breast arterial calcification (BAC) is calcium deposition in peripheral arterioles. There is increasing evidence that BAC is a good indicator of the risk of cardiovascular disease. The accurate and automated detection of BACs in mammograms remains an unsolved task and the technology is far from clinical deployment. The challenging task is to develop an explainable model applicable to BAC detection, able to discriminate between severe and weak BACs in patients’ images.

Autonomous car makes me sick

Contact:  palaniappan ramaswamy.

With the rapid advancements in autonomous car technology, we will soon see cars driving on their own on the roads. While some may dread this lack of control in fear of safety, generally it is much safe and the real issue lies elsewhere. Do you know that many of us will feel sick – motion sickness will become a huge problem and there is not much ongoing work to mitigate this situation.  In this project, we will explore using transcutaneous auricular vagus nerve stimulation (taVNS) as an intervention technology. VNS is a medically approved technology for conditions such as epilepsy. But here we will study the non-invasive version of VNS in mitigating the effects of motion sickness. Functional near infra-red spectroscopy (fNIRS) will be utilised to assess the effect of the taVNS on motion sickness. Some prior signal processing knowledge will be required but knowledge on VNS and fNIRS can be gained from the project. 

Stress management

The fundamental aspect of human experience is awareness. Combined with the ability to think, imagine and understand it results into the beautiful cosmic play we experience. However, with it comes along a multitude of problems, often illusory in nature – such as stress, anxiety, anger, negativity, etc. It isn’t hard to guess that in such states our behaviour is significantly altered, usually in harmful ways for both – us and the environment. There are techniques such as meditation, music, humour which can help us come back to our “real” senses and feel happier/peaceful again. So the fundamental enquiry would be about what sort of things do help us achieve a happier state, and moreover what’s their impact on both short term and long term brain functioning. This project will study this aim using EEG.

Information Visualisation Directed by Graph Data Mining

Contact:  peter rodgers.

Data visualisation techniques are failing in the face of large data sets. This project attempts to increase the scale of graph data that can be visualised by developing data mining techniques to guide interactive visualisation. This sophisticated combining of information visualisation and data mining promises to greatly improve the size of data understandable by analysts, and will advance the state of the art in both disciplines. On successful completion, publications in high quality venues are envisaged. This project is algorithmically demanding, requiring good coding skills. The implementation language is negotiable, but Java, JavaScript or C++ are all reasonable target languages. Data will be derived from publicly available network intrusion or social network data sets. Tasks in this research project include: (1) implementing graph display software and interface. (2) developing project specific visualisation algorithms. (3) integrating graph pattern matching and other graph data mining systems into the visualisation algorithms.

Visual Analytics for Set Data

Visual Analytics is the process of gaining insights into data through combining AI and information visualization. At present, visual analytics for set based data is largely absent. There are a large number of sources for set based data, including social networks as well as medical and biological information. This project will look at producing set mining algorithms which can then be used to support set visualization methods such as Euler/Venn diagrams or Linear diagrams. Firstly, the use of existing data mining methods will produce useful information about sets and the data instances in them. After this effort, more complex algorithms for subset and set isomorphism will be developed to allow for pattern matching within set data. These set mining methods will be integrated into Euler diagram based exploratory set visualization techniques.

Using Soft Nanomembrane Electronics for Home-based Anxiety Monitoring

Contact:  jim ang.

Sensor-enhanced virtual reality systems for mental health care and rehabilitation. New immersive technologies, such as  virtual reality (VR) and augmented reality (AR) are playing an increasingly important role in the digital health revolution. Significant research has been carried out at University of Kent, in collaboration with medical scientists/practitioners, psychiatrists/psychologists, digital artists and material scientists (for novel sensor design and integration with VR). Such projects include designing VR for dementia care, eating disorder therapy, eye disorder therapy and VR-enabled brain-machine interactions. This PhD research can take on the following directions: (1) Co-design of VR for a specific healthcare domains, involving key stakeholders (e.g. patient representatives, clinicians, etc) to  understand the design and deployment opportunities and challenges in realistic health contexts. (2) Deploy and evaluate VR prototypes to study the impact of the technologies in the target groups. (3) Design and evaluate machine learning algorithms to analyse behavioural and physiological signals for clinical meaningful information, e.g. classification of emotion, detection of eye movement, etc. 

Relevant publications: 

[1] M Mahmood, S Kwon, H Kim, Y Kim, P Siriaraya, J Choi, B Otkhmezuri, K Kang, KJ Yu, YC Jang, CS Ang, W Yeo (2021) Wireless Soft Scalp Electronics and Virtual Reality System for Motor Imagery ‐ Based Brain–Machine Interfaces. Advanced Science. 8(19). 

[2] S Mishra, K Yu, Y Kim, Y Lee, M Mahmood, R Herbert, CS Ang, W Yeo, J Intarasirisawat, Y Kown, H Lim (2020). Soft, wireless periocular wearable electronics for real-time detection of eye vergence in a virtual reality toward mobile eye therapies. Science Advances. 6 (11), eaay1729. 

[3] L Tabbaa, CS Ang, V Rose, P Siriaraya, I Stewart, KG Jenkins, M Matsangidou (2019) Bring the Outside In: Providing Accessible Experiences Through VR for People with Dementia in Locked Psychiatric Hospitals, Proceedings of the CHI 2019 Conference on Human Factors in Computing Systems. 

[4] M Matsangidou, B Otkhmezuri, CS Ang, M Avraamides, G Riva, A Gaggioli, D Iosif, M Karekla (2020). “Now I can see me” designing a multi-user virtual reality remote psychotherapy for body weight and shape concerns. Human–Computer Interaction. 1-27.

Optimisation of Queries over Virtual Knowledge Graphs

Contact:  elena botoeva.

Virtual Knowledge Graphs (also known as Ontology-Based Data Access) provide user-friendly access to Big Data stored in (possibly multiple) data sources, which can be traditional relational ones or more novel ones such as document and triple stores. In this framework an ontology is used as a conceptual representation of the data, and is connected to the data sources by the means of a mapping. User formulates queries over the ontology using a high-level query language like SPARQL; user queries are then automatically translated into queries over the underlying data sources, and the latter are executed by the database engines. Efficiency of the whole approach is highly dependent on optimality of the data source queries. While the technology is quite developed when the underlying data sources are relational, there are still many open problems when it comes to novel data sources, such as MongoDB, graph databases etc. The objective of this PhD project is to design novel techniques for optimising data source queries arising in the context of Virtual Knowledge Graphs.

Heuristics for Scalable Verification of Neural Networks

Due to the success of Deep Learning neural networks are now being employed in a variety of safety-critical applications such as autonomous driving cars and aircraft landing. Despite showing impressive results at various tasks, neural networks are known to be vulnerable (hence, not robust) to adversarial attacks: imperceptible to human eye perturbations to an input can lead to incorrect classification. Robustness verification of neural networks is currently a very hot topic both in academia and industry as neural networks. One of the main challenges in this field is deriving efficient techniques that can verify networks with hundred thousands / millions of neurons in reasonable time, which is not trivial given that exact verification is not tractable (NP- or coNP-complete for ReLU-based neural networks depending on the exact verification problem). Incomplete approaches generally offer better scalability but at the cost of completeness. The aim of the proposed PhD project will be to learn heuristics for efficient verification of neural networks.

Understanding Spiking Neural Networks

Contact:  dominique chu.

Spiking Neural Networks (SNN) are brain-like neural networks. Unlike standard rate coding neural networks, signals are encoded in time. This makes them ideal for processing data that has a temporal component, such as time-series data, video or music. Another advantage of SNNs is that there exists neuromorphic hardware that can efficiently simulate SNNs. SNNs are generally thought to be “more powerful” than standard rate coding networks. However, it is not clear precisely in what sense they are more powerful, or what precisely it is that makes them more powerful. The idea of this project is to investigate this claim using a combination of mathematical and computational methods. As such the project will require an interdisciplinary research methodology at the interface between mathematics, computer science and neuroscience. The project would be suitable for a student who wishes to become and expert in an up-and-coming method in artificial intelligence. It has the scope for both theoretical investigations, but will also require implementing neural networks.

Training algorithms for spiking neural networks

Spiking neural networks encode information through the temporal order of the signals. They are more realistic models of the brain than standard artificial neural networks and they are also more efficient in encoding information. Spiking neural networks are therefore very popular in brain simulations. A disadvantage of spiking neural networks is that there are not many efficient training algorithms available. This project will be about finding novel training algorithms for spiking neural networks and to compare the trained networks with standard artificial neural networks on a number of benchmark AI tasks. An important part of this project will be not only to evaluate how well these spiking neural networks perform in relation to standard networks, but also to understand whether or not they are, as is often claimed, more efficient in the sense that they need smaller networks or fewer computing resources. The main approach of the model will be to gain inspiration from existing theories about how the how the human brain develops and learns. These existing theories will then be adapted so as to develop efficient training algorithms. This project will be primarily within AI, but it will also provide the opportunity to learn and apply techniques and ideas from computational neuroscience.

Machine learning systems to improve medical diagnosis

Contact:  daniel soria.

Research shows that machine learning methods are extremely useful to discover or identify patterns that can help clinicians to tailor treatments. However, the implementation of those data mining procedures may be challenging because of high dimensional data sets, and the choice of proper machine learning methods may be tricky. 

The aim of the research project will be to design and develop new intelligent machine learning systems with high degree of flexibility suitable for disease prediction/diagnosis, that are also easily understandable and explicable to non-experts in the field. Data will be sought from the UK Biobank, to examine whether the selected features are correlated with the occurrence of specific diseases (e.g., breast cancer), whether these relationships persist in the presence of covariates, and the potential role of comorbidities (e.g., obesity, diabetes and cardiovascular diseases) in the assessment of the developed models

How creative are crime-related texts and what does this tell us about cyber crime?

Contact:  shujun li ,   anna jordanous.

The main aim of the PhD project is to investigate if crime-related texts can be evaluated in terms of creativity using automatic metrics. Such a study will help understand how crime-related texts are crafted (by criminals and by automated tools, possibly via a hybrid human-machine teaming approach), how they have evolved over time, how they are perceived by human receivers, and how new methods can be developed to educate people about tactics of cyber criminals. The four tasks of the PhD project will include the following: (1) collecting a large datasets of crime-related texts; (2) developing some objective (automatable) creativity metrics using supervised machine learning, targeted towards evaluating the creativity of crime-related texts (e.g., phishing emails, online hate speech, grooming, cyber bullying, etc.); (3) applying the creativity metrics to the collected data to see how malevolent creativity has evolved over years and for different crimes; (4) exploring the use of generative AI algorithms to create more creative therefore more deceptive crime-related texts.

Computational creativity and automated evaluation

Contact:  anna jordanous.

In exploring how computers can perform creative tasks, computational creativity research has produced many systems that can generate creative products or creative activity. Evaluation, a critical part of the creative process, has not been employed to such a great extent within creative systems. Recent work has concentrated on evaluating the creativity of such computational systems, but there are two issues. Firstly, recent work in evaluation of computational creativity has consisted of the system(s) being evaluated by external evaluators, rather than by the creative system evaluating itself, or evaluation by other creative software agents that may interact with that system. Incorporation of self-evaluation into computational creativity systems *as part of guiding the creative process* is also under explored. In this project the candidate will experiment with incorporating evaluation methods into a creative system and analyse the results to explore how computational creativity systems can incorporate self-evaluation. The creative systems studied could be in the area of musical or linguistic creativity, or in a creative area of the student’s choosing. It is up to the student to decide whether to focus on evaluation methods for evaluating the quality of output from a creative system or the creativity of the system itself (or both). The PhD candidate would be required to propose how they would will explore the above scenarios, for a more specific project. Anna is happy to guide students in this and help them develop their research proposal.

Expressive musical performance software

Traditionally, when computational software performs music the performances can be criticised for being too unnatural, lacking interpretation and, in short, being too mechanical. However much progress has been made within the field of expressive musical performance and musical interpretation expression. Alongside these advances have been interesting findings in musical expectation (i.e. what people expect to hear when listening to a piece of music), as well as work on emotions that are present within music and on how information and meaning are conveyed in music. Each of these advances raises questions of how the relevant aspects could be interpreted by a musical performer. Potential application areas for computer systems that can perform music in an appropriately expressive manner include, for example, improving playback in music notation editors (like Sibelius), or the automated performance of music generated on-the-fly for ‘hold’ music (played when waiting on hold during phone calls). Practical work exploring this could involve writing software that performs existing pieces, or could be to write software that can improvise, interpreting incoming sound/music and generating an appropriate sonic/musical response to it in real time.

Brain-like Computer  

Contact:  frank wang.

The human brain consists of about one billion neurons. Each neuron forms about 1,000 connections to other neurons, amounting to more than a trillion connections. If each neuron could only help store a single memory, running out of space would be a problem. You might have only a few gigabytes of storage space, similar to the space in an iPod or a USB flash drive. Yet neurons combine so that each one helps with many memories at a time, exponentially increasing the brain’s memory storage capacity to something closer to around 2.5 petabytes (or a million gigabytes). The way our brain organizes data may help us manage continuously increasing data, especially in Cloud computing and Big Data. In this project, you are expected to simulate a brain-like computer. Such a computer should be categorised into the unconventional computer group, which is different from traditional Turing machine (with stored programmes) or Von Neumann computer (with an operating system).

My relevant papers: Adaptive Neuromorphic Architecture , Memristor Neural Networks , Grid-Oriented Storage (IEEE Transactions on Computers) .

My relevant keynote talk at Cambridge: Brain and Brain-Inspired Artificial Intelligence )

New Quantum Computer

Contact: frank wang.

Most recently, Frank Wang published an article on Quantum Information Processing (Springer Nature) to report a new quantum computer that can break Landauer’s Bound. Among a number of physical limits to computation, Landauer’s bound limits the minimum amount of energy for a computer to process a bit of information. In light of this study, we may have to presume the demise of this bound despite the many mysteries uncovered with it over the past 60 years.

My relevant papers: Breaking Landauer’s bound in a spin-encoded quantum computer (Springer Nature) , Can We Break the Landauer Bound in Spin-Based Electronics (IEEE Access) .

My relevant keynote talk at Cambridge: A New Quantum Computer Not Bound By Landauer’s Bound )

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PhD Artificial Intelligence

Year of entry: 2025

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The standard academic entry requirement for this PhD is an upper second-class (2:1) honours degree in a discipline directly relevant to the PhD (or international equivalent) OR any upper-second class (2:1) honours degree and a Master’s degree at merit in a discipline directly relevant to the PhD (or international equivalent).

For more information about applications, please visit the CDT in AI for Decision Making in Complex Systems website .

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Please visit the CDT in AI for Decision Making in Complex Systems website for more information about applications.

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Please note: We are only accepting applications for PhD in Artificial Intelligence through the Centre for Doctoral Training (CDT) in AI for Decision Making in Complex Systems.

The Centre for Doctoral Training (CDT) in AI for Decision Making in Complex Systems is a 4-year programme that will educate the next generation of AI researchers to develop and deploy new machine learning models that can efficiently cope with uncertainty in complex systems.

Bringing together researchers in machine learning from the universities of Manchester and Cambridge, the CDT will be grounded in the research areas of physics and astronomy, engineering, biology, and material science, as well as a cross-cutting theme of using AI to increase business productivity, ultimately applying the research to real-world scenarios.

For more information, visit the CDT in AI for Decision Making in Complex Systems website .

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Please visit the CDT in AI for Decision Making in Complex Systems website for more information about fees and funding.

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Academic entry qualification overview, application and selection, how to apply, interview requirements, programme details, programme description.

The Centre for Doctoral Training (CDT) in AI for Decision Making in Complex Systems is a 4-year programme that will educate the next generation of AI researchers to develop and deploy new machine learning models that can efficiently cope with uncertainty in complex systems. Bringing together researchers in machine learning from the universities of Manchester and Cambridge, the CDT will be grounded in the research areas of physics and astronomy, engineering, biology, and material science, as well as a cross-cutting theme of using AI to increase business productivity, ultimately applying the research to real-world scenarios.

For more information, please visit the CDT in AI for Decision Making in Complex Systems website .

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RESEARCH PROPOSAL TO SPECIALISTS IN ARTIFICIAL INTELLIGENCE AND ROBOTICS TECHNOLOGY

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We have 18 Artificial Intelligence (cyber security) PhD Research Projects PhD Projects, Programmes & Scholarships

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Artificial Intelligence (cyber security) PhD Research Projects PhD Projects, Programmes & Scholarships

Digital twins in cyber security analysis of connected and autonomous vehicles (dtcs-cav), phd research project.

PhD Research Projects are advertised opportunities to examine a pre-defined topic or answer a stated research question. Some projects may also provide scope for you to propose your own ideas and approaches.

Self-Funded PhD Students Only

This project does not have funding attached. You will need to have your own means of paying fees and living costs and / or seek separate funding from student finance, charities or trusts.

Cyber Security, Artificial Intelligence, Machine Learning and Blockchain Technology: Mitigating Cyber Attacks and Detecting Malicious Activities in Network Traffic

Cyber security and resilience, funded phd project (students worldwide).

This project has funding attached, subject to eligibility criteria. Applications for the project are welcome from all suitably qualified candidates, but its funding may be restricted to a limited set of nationalities. You should check the project and department details for more information.

Secure Code Generation with Foundation Models

Ai-driven cloud security: advancing sustainable and zero trust frameworks through automation, cybersecurity risk assessment and mitigation in artificial intelligence life cycle, information leakage control in machine learning models for privacy assurance, enhancing cybersecurity through automated real-time threat response phd, software defined networking (sdn) security solutions for small and medium-sized enterprises (smes), user-centric privacy-by-design ai-based adaptive access control in a healthcare robot for the elderly, epsrc centre for doctoral training (cdt) in future open secure networks (fort) - jointly with queen’s university belfast, funded phd project (uk students only).

This research project has funding attached. It is only available to UK citizens or those who have been resident in the UK for a period of 3 years or more. Some projects, which are funded by charities or by the universities themselves may have more stringent restrictions.

Adaptive Multi-Access Interoperable Communication Fabric for TinyEdge

Advancing software development and maintenance through foundation models, hardening the computing continuum against adversarial attacks, engd in ai assurance and verification.

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 RESEARCH PROPOSAL IN ARTIFICIAL INTELLIGENCE

Artificial intelligence (AI) has become one of the most exciting fields to work with. Do you want to gain expertise advice for your research proposal. Our developers and writers give a proper solution to all your problems in AI. At first, we help you to choose the best topics, you need to have for initial research. We work in all the aspects of a topic and pick out the most specific issue in that place. Thus, we keep up our work on the right track. Our researchers go through, benchmark references, journals for collecting information based on your research objective.

Some of the key points we consider for best research proposal are:

• Find a Research Problem:

This is the first step in writing a research proposal here we find a problem of the proposed field of artificial intelligence that you want to address. It can be based on a specified challenge or limitation in AI algorithms, a demand for a more well-organized AI systems, or a longing to reconsider the ethical implications of AI technology. A clear-cut definition of the research problem and explanation why it is important will be mentioned.

• Conduct a Literature Review:

Before going into research, we review the existing literature in that field based on artificial intelligence. So that the current state of knowledge can be understand, existing research gaps can be categorized. The key findings and methodologies of related studies based on your proposal will be analyzed.

• Define Objectives and Research Questions:

Thus, the objectives of the research will be clearly stated out based on the research problem and literature review. What we hope to attain? What are the research questions that we are going to address? These objectives and research questions stands as a guide and provide a clear focus for your research methodology for our proposal.

• Choose a Research Methodology:

The methodology we use to carry your research work will be determined. The question comes as Should we focus on theoretical analysis, experimental studies, or a grouping of both? Being updated on daily basis we will always have the proper resources and feasibility of chosen methodology.

• Develop a Timeline and Budget:

Here we also include a timeline outlining at each step and a budget estimation for a proposal. We will pause our research into controllable tasks and assign time frames at each step. Potential challenges or obstacles that may arise will be studied. More over evaluate the costs for your research, data collection, or software requirements.

• Highlight Expected Results and Impact:

Clear the potential of our research work. What are our expected findings or contributions to the area of artificial intelligence? Will our research produce practical applications or theoretical implications? The meaning of our work will be explained, how it brings into line with current trends in AI.

• Write a Compelling Proposal:

A clear and concise proposal will be written to that communicates our research plan. Formatting, grammar, and style will be considered so our   proposal is professional and easy to read. Headings and subheadings will be used to form your content and make it more manageable. Feedback from leading experts will be got to improve our clarity of our proposal.

In our wide research writing journey, the research proposal is the first step. Constant revisions will be carried on to achieve a tremendous success of your journey. phdprojects.org is a gateway for enriching your academic voyage. Thus, we modify your proposal in such a  way that it is exactly to your research objectives, we assure its credibility, and including relevant data collection methods, techniques analysis and its ethical considerations.

ARTIFICIAL INTELLIGENCE THESIS PROPOSALS

AI thesis proposal are well carved by our professional writers. If you do thesis proposal with us, we assure you that our work, paves the way for your successful and approved research endeavor. Trust on our expertise to support you throughout each phase of your thesis proposal journey. Thus, we assure you to present an outstanding proposal across all sub fields of AI domains by using latest methodologies. We frame our own proposal or we even customize thesis proposal as per your needs.

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Archived funding opportunity

Nsf 23-605: graduate research fellowship program (grfp), program solicitation, document information, document history.

• Posted: July 18, 2023
• Replaces: NSF 22-614
• Replaced by: NSF 24-591

Program Solicitation NSF 23-605

Application Deadline(s) (received by 5 p.m. local time of applicant’s mailing address):

     October 16, 2023

Life Sciences

     October 17, 2023

Computer and Information Science and Engineering, Materials Research, Psychology, Social Sciences, STEM Education and Learning

     October 19, 2023

Engineering

     October 20, 2023

Chemistry, Geosciences, Mathematical Sciences, Physics and Astronomy

Important Information And Revision Notes

• This solicitation covers the Fiscal Year (FY) 2024 competition.
• Applicants must use the Research.gov/GRFP site ( https://www.research.gov/grfp/Login.do ) to register in Research.gov and submit their applications through the GRFP Application Module. Do not send application materials outside of the GRFP Application Module.
• Applications are due on the deadline date at 5:00 p.m. local time of the applicant’s mailing address.
• Currently enrolled second-year graduate students are strongly advised to provide official Registrar-issued transcripts as part of their application.
• NSF will continue to emphasize high priority research in alignment with the priorities laid out in pages 127-128 of the FY2024 budget https://www.whitehouse.gov/wp-content/uploads/2023/03/budget_fy2024.pdf
• Portions of the eligibility criteria have been rewritten for clarity.
• Reference letter writers must use the Research.gov/GRFP site ( https://www.research.gov/grfp/Login.do ) to register in Research.gov and submit reference letters through the Reference Letter System. Reference letters are due October 27 at 5:00 p.m. Eastern Time (ET).
• Applicants and reference letter writers requiring accessibility accommodation are asked to notify the GRF Operations Center at least four weeks before the deadline to coordinate assistance with NSF in submitting the application or reference letter.

Summary Of Program Requirements

General information.

Program Title:

NSF Graduate Research Fellowship Program (GRFP)

The purpose of the NSF Graduate Research Fellowship Program (GRFP) is to help ensure the quality, vitality, and diversity of the scientific and engineering workforce of the United States. The program recognizes and supports outstanding graduate students who are pursuing full-time research-based master's and doctoral degrees in science, technology, engineering, and mathematics (STEM) or in STEM education. The GRFP provides three years of support over a five-year fellowship period for the graduate education of individuals who have demonstrated their potential for significant research achievements in STEM or STEM education. NSF actively encourages submission of applications from the full spectrum of diverse talent in STEM. NSF GRFP was established to recruit and support individuals who demonstrate the potential to make significant contributions in STEM. Thus, NSF especially encourages applications from undergraduate seniors and Bachelor's degree-holders interested in pursuing research-based graduate study in STEM. First- and second-year graduate students in eligible STEM fields and degree programs are also encouraged to apply.

Cognizant Program Officer(s):

Please note that the following information is current at the time of publishing. See program website for any updates to the points of contact.

Contact: GRF Operations Center, telephone: (866) 673-4737, email: [email protected]

• 47.041 --- Engineering
• 47.049 --- Mathematical and Physical Sciences
• 47.050 --- Geosciences
• 47.070 --- Computer and Information Science and Engineering
• 47.074 --- Biological Sciences
• 47.075 --- Social Behavioral and Economic Sciences
• 47.076 --- STEM Education
• 47.079 --- Office of International Science and Engineering
• 47.083 --- Office of Integrative Activities (OIA)
• 47.084 --- NSF Technology, Innovation and Partnerships

Award Information

Anticipated Type of Award:

Estimated Number of Awards: 2,500

NSF will support at least 2,500 new Graduate Research Fellowships per fiscal year under this program solicitation pending availability of funds.

Anticipated Funding Amount: $159,000

Per award (Fellowship), pending the availability of funds.

Each Fellowship provides three years of support over a five-year fellowship period. For each of the three years of support, NSF provides a $37,000 stipend and $16,000 cost of education allowance to the graduate degree-granting institution of higher education for each Fellow who uses the support in a fellowship year. The Fellowship is portable and can be transferred to a different institution of higher education if a Fellow chooses to transfer to another institution after completion of the first Fellowship year. While the Fellowship is offered to the individual, the Fellowship funds are awarded to the institution of higher education at which a Fellow is enrolled and the institution is responsible for disbursement of the stipend to the Fellow.

Eligibility Information

Organization Limit:

Fellowship applications must be submitted by the prospective Fellow. Applicants must use the GRFP application module in Research.gov ( https://www.research.gov/grfp/Login.do ) to submit the application. Confirmation of acceptance in a graduate degree program in STEM or STEM education is required at the time of Fellowship acceptance, no later than the deadline indicated in the fellowship offer letter, of the year the Fellowship is accepted. Prospective Fellows must enroll in a non-profit university, college, or institution of higher education accredited in, and having a campus located in, the United States, its territories or possessions, or the Commonwealth of Puerto Rico that offers advanced degrees in STEM and STEM education no later than fall of the year the Fellowship is accepted. All Fellows from the date of Fellowship Start through Completion or Termination of the Fellowship must be enrolled in a graduate degree-granting institution of higher education accredited in, and having a campus located in, the United States its territories or possessions, or the Commonwealth of Puerto Rico.

See the Detailed Eligibility Requirements in Section IV for full information. Eligibility is based on the applicant's status at the application deadline. Applicants must self-certify that they are eligible to receive the Fellowship. To be eligible, an applicant must meet all of the following eligibility criteria at the application deadline: Be a U.S. citizen, national, or permanent resident Intend to enroll or be enrolled full-time in a research-based Master's or doctoral degree program in an eligible Field of Study in STEM or STEM education (See Appendix and Section IV.3 for eligible Fields of Study) Have completed no more than one academic year (according to institution's academic calendar) while enrolled in a graduate degree program Never previously accepted a Graduate Research Fellowship Declined any previously offered Graduate Research Fellowship by the acceptance deadline Never previously applied to GRFP while enrolled in a graduate degree program Never earned a doctoral or terminal degree in any field Individuals holding joint Bachelor's-Master's degrees who did not progress directly to a doctoral program the semester following award of the joint degree must apply as returning graduate students (see below) Individuals with prior graduate enrollment who have: (i) completed more than one academic year in any graduate degree-granting program, (ii) earned a previous master's degree of any kind (including Bachelor's-Master's degree), or (iii) earned a professional degree must meet the following requirements: not enrolled in a graduate degree program at application deadline two or more consecutive years past graduate degree enrollment or completion at the application deadline Not be a current NSF employee Number of Times An Individual May Apply Undergraduate seniors and Bachelor's degree holders who have never enrolled in a graduate degree program have no restrictions on the number of times they can apply before enrolling in a degree-granting graduate program. Currently enrolled graduate students who have completed no more than one academic year (according to institution's academic calendar) while enrolled in a graduate degree program can apply only once . Non-degree coursework does not count toward the one academic year limit. Individuals applying while enrolled in a joint Bachelor's-Master's degree program are considered graduate students who: i) must have completed three (3) years in the joint program, and; ii) are limited to one application to GRFP; they will not be eligible to apply again as doctoral students. For GRFP, joint Bachelor's-Master's degrees are defined as degrees concurrently pursued and awarded . Individuals holding joint Bachelor's-Master's degrees, currently enrolled as first-year doctoral students, who (i) have not previously applied as graduate students and (ii) enrolled in the doctoral program the semester following award of the joint degree, may only apply in the first year of the doctoral program. Applications withdrawn by November 15 of the application year do not count toward the one-time graduate application limit. Applications withdrawn after November 15 count toward this one-time limit. Applications not reviewed by NSF do not count toward the one-time graduate application limit.

An eligible applicant may submit only one application per annual competition.

Application Preparation and Submission Instructions

A. application preparation instructions.

Letters of Intent: Not applicable

Preliminary Proposal Submission: Not applicable

Application Instructions: This solicitation contains information that deviates from the standard NSF Proposal and Award Policies and Procedures Guide (PAPPG) proposal preparation guidelines. Please see the full text of this solicitation for further information.

B. Budgetary Information

Cost Sharing Requirements:

Inclusion of voluntary committed cost sharing is prohibited.

Indirect Cost (F&A) Limitations:

No indirect costs are allowed.

Other Budgetary Limitations:

Other budgetary limitations apply. Please see the full text of this solicitation for further information.

C. Due Dates

Application review information criteria.

Merit Review Criteria:

National Science Board approved Merit Review Criteria (Intellectual Merit and Broader Impacts) apply. Additional Solicitation-Specific Review Criteria also apply (see Section VI.A below).

Award Administration Information

Award Conditions:

NSF GRFP awards are made to the institution of higher education at which a Fellow is or will be enrolled. The awardee institution is responsible for financial management of the award and disbursement of Fellowship funds to the individual Fellow. The institution will administer the awards, including any amendments, in accordance with the terms of the Agreement and provisions (and any subsequent amendments) contained in the document NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials . All Fellowships are subject to the provisions (and any subsequent amendments) contained in the document NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials .

Reporting Requirements:

See reporting requirements in full text of solicitation and the NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials . Fellows are required to submit annual activity reports and to declare fellowship status by the deadline specified in the notification sent by email each year. Additional reporting requirements are presented in Section VII.C of this solicitation.

I. Introduction

The Graduate Research Fellowship Program (GRFP) is a National Science Foundation-wide program that provides Fellowships to individuals selected early in their graduate careers based on their demonstrated potential for significant research achievements in science, technology, engineering or mathematics (STEM) or in STEM education. Three years of support over a five-year period are provided for graduate study that leads to a research-based master's or doctoral degree in STEM or STEM education (see eligible Fields of Study in Appendix).

The program goals are: 1) to select, recognize, and financially support early-career individuals with the demonstrated potential to be high achieving scientists and engineers, and 2) to broaden participation of the full spectrum of diverse talents in STEM. NSF actively encourages submission of applications from the full spectrum of diverse talent in STEM.

GRFP is a critical program in NSF's overall strategy to develop the globally-engaged workforce necessary to ensure the Nation's leadership in advancing science and engineering research and innovation. The ranks of NSF Fellows include numerous individuals who have made transformative breakthrough discoveries in science and engineering, become leaders in their chosen careers, and been honored as Nobel laureates.

II. Program Description

The Graduate Research Fellowship Program (GRFP) awards Fellowships for graduate study leading to research-based master's and doctoral degrees in STEM or in STEM education. GRFP supports individuals proposing a comprehensive plan for graduate education that takes individual interests and competencies into consideration. The plan describes the academic achievements, attributes, and experiences that illustrate the applicant's demonstrated potential for significant research achievements. The applicant must provide a detailed profile of their relevant education, research experience, and plans for graduate education that demonstrates this potential.

Prospective applicants are advised that submission of an application implies their intent to pursue graduate study in a research-based program in STEM or STEM education at an accredited, non-profit institution of higher education having a campus located in the United States, its territories or possessions, or the Commonwealth of Puerto Rico. All applicants are expected to either have adequate preparation to enroll in a research-based master's or doctoral program, or be enrolled in such a program by fall of the year the Fellowship is accepted. From the date of the Fellowship Start through Completion or Termination of the Fellowship, applicants accepting the award (Fellows) must be enrolled in an accredited graduate degree-granting institution of higher education having a campus located in the United States, its territories or possessions, or the Commonwealth of Puerto Rico.

In FY2024, NSF will continue to fund outstanding Graduate Research Fellowships in all areas of science and engineering supported by NSF and continue to emphasize high priority research areas in alignment with NSF goals and priorities listed in pages 127-128 of the FY2024 budget ( https://www.whitehouse.gov/wp-content/uploads/2023/03/budget_fy2024.pdf ). Applications are encouraged in all disciplines supported by NSF.

III. Award Information

Fellowship funding will be for a maximum of three years of financial support (in 12-month allocations, starting in fall or summer) usable over a five-year fellowship period. The anticipated announcement date for the Fellowship awards is early April each year.

The Fellowship is portable and can be transferred to a different institution of higher education if a Fellow chooses to transfer to another institution after completion of the first Fellowship year. While the Fellowship is offered to the individual, the Fellowship funds are awarded to the institution at which a Fellow is enrolled and is considered the official NSF awardee institution. The awardee institution receives up to a $53,000 award per Fellow who uses the support in a fellowship year. The awardee institution is responsible for disbursement of fellowship funds to the Fellow. The Graduate Research Fellowship stipend is $37,000 for a 12-month tenure period, prorated in whole month increments of $3,083. The Cost of Education allowance provides payment in lieu of tuition and mandatory fees to the institution of $16,000 per year of fellowship support.

During receipt of the fellowship support, the institution is required to exempt Fellows from paying tuition and fees normally charged to students of similar academic standing, unless such charges are optional or are refundable (i.e., the institution is responsible for tuition and required fees in excess of the cost-of-education allowance). Acceptance of fellowship funds by the awardee institution indicates acceptance of and adherence to these and other terms and conditions of the NSF GRFP award. Refer to NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials for restrictions on the use of the cost-of-education allowance.

GRFP awards are eligible for supplemental funding as described in Chapter VI of the NSF Proposal & Award Policies & Procedures Guide (PAPPG) ( NSF 23-1 ).

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects as described in Chapter II.F of the PAPPG . Fellows with disabilities may apply for assistance after consulting the instructions in the document NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials.

Career-Life Balance Supplemental Funding Requests (Dear Colleague Letter NSF 21-021 ) can be requested by the awardee institution to provide additional personnel (e.g., technician) to sustain the research of Fellows on approved medical leave due to family leave situations.

Fellows are eligible to apply for non-academic INTERN supplements following guidance specific to GRFP.

Honorable Mention

The NSF accords Honorable Mention to meritorious applicants who do not receive Fellowship offers. This is considered a significant national academic achievement.

IV. Eligibility Information

Applicant Eligibility:

Limit on Number of Applications per Applicant: 1

Additional Eligibility Info:

Eligibility is based on the applicant's status at the application deadline. Detailed Eligibility Requirements: Described in detail below are the eligibility requirements for the Graduate Research Fellowship Program: (1) citizenship, (2) degree requirements, and (3) field of study, degree programs, and proposed research. Applicants are strongly advised to read the entire program solicitation carefully to ensure that they understand all the eligibility requirements. Applicants must self-certify that they meet all eligibility criteria. 1. Citizenship Applicants must be United States citizens, nationals, or permanent residents of the United States by the application deadline. The term "national" designates a native resident of a commonwealth or territory of the United States. It does not refer to a citizen of another country who has applied for United States citizenship and who has not received U.S. citizenship by the application deadline, nor does it refer to an individual present in the U.S. on any type of visa. 2. Degree Requirements Applicants are eligible to apply: 1) as current undergraduates, or Bachelor's degree holders who have never enrolled in a degree-granting graduate program, and who will be prepared to attend graduate school in fall of the award year; 2) as current graduate students who have not completed more than one academic year (according to institution's academic calendar) of any degree-granting graduate program; or 3) as returning graduate students who are not currently enrolled and who have had an interruption of at least two consecutive years in graduate study since their most recent enrollment in any graduate degree-granting program, regardless of whether the degree was completed or awarded. Below are detailed guidelines to determine eligibility: a) Applicants not currently enrolled in a graduate degree program, with no prior enrollment in a graduate degree-granting program (including joint Bachelor's-Master's programs): With no prior graduate degree program enrollment Undergraduate students on track to receive a Bachelor's degree by the fall of the year following the application (e.g., senior or final year of Bachelor's degree) and Bachelor's degree holders never enrolled in a graduate degree program can apply an unlimited number of times prior to enrolling in a graduate degree program. They must be prepared to enroll in a full-time graduate degree program by fall of the year they are offered a Graduate Research Fellowship. With one year or less of prior graduate degree-granting program enrollment Applicants must not have completed more than one academic year (according to institution's academic calendar) of graduate study as indicated in the academic transcript issued by the Registrar of the universities attended as of the application deadline (see exception below). Applicants re-entering graduate study : applicants who have completed more than one academic year (according to institution's academic calendar) of graduate study or earned a previous Master's or professional degree are eligible only if they have had an interruption in graduate study of at least two consecutive years immediately prior to the application deadline, and are not enrolled in a graduate program at the deadline . Applicants must not have engaged in any graduate coursework during the interruption. Applicants should address the reasons for the interruption in graduate study in the Personal, Relevant Background and Future Goals Statement. b) Applicants pursuing a Master's degree concurrently with a Bachelor's degree (joint Bachelor's-Master's degree program in which both degrees are awarded at the same time as indicated on the transcript): Individuals applying while enrolled in a joint Bachelor's-Master's degree program are considered graduate students, who: 1) must have completed three years in the joint program, and; ii) are limited to one application to GRFP; they will not be eligible to apply again as doctoral students. Individuals holding joint Bachelor's-Master's degrees, currently enrolled as first-year doctoral students, who have not previously applied as graduate students and enrolled in the doctoral program the semester following award of the joint degree, may only apply in the first year of the doctoral program. Individuals holding joint Bachelor's-Master's degrees who did not progress directly to a doctoral program the semester following award of the joint degree must apply as returning graduate students (see above). c) Applicants currently enrolled in a graduate degree program: Applicants must not have completed more than one academic year of graduate study as indicated in the academic transcript issued by the Registrar of the universities attended, as of the application deadline. Participation in non-degree summer activities PRIOR TO graduate status as indicated in the academic transcript issued by the Registrar before the start of the fall graduate program is not included in this total. Graduate status is understood to begin on the date indicated on the Registrar-issued transcript and ALL activities after that date will be considered graduate activities. Second-year graduate students are strongly advised to include official Registrar-issued transcripts with their application. If the transcript does not clearly indicate the start date of graduate status, applicants are strongly advised to include documents from the Registrar confirming the start of their graduate status. Graduate coursework taken without being enrolled in a graduate degree-granting program is not counted in this limit. 3. Field of Study, Degree Programs, and Proposed Research Fellowships are awarded for graduate study leading to research-based Master's and doctoral degrees in science, technology, engineering or mathematics (STEM) or in STEM education, in eligible Fields of Study listed below: Chemistry Computer and Information Sciences and Engineering Engineering Geosciences Life Sciences Materials Research Mathematical Sciences Physics & Astronomy Psychology Social, Behavioral, and Economic Sciences STEM Education and Learning Research A complete list of eligible Major Fields of Study and their subfields are listed in the Appendix. If awarded, Fellows must enroll in a graduate degree program consistent with the Major Field of Study proposed in their application. A fellowship will not be awarded in a different Major Field of Study from that indicated in the application. Only research-based Master's and doctoral degrees in STEM or STEM education are eligible for GRFP support. Professional degree programs and graduate programs that are primarily course-based with no thesis are ineligible for GRFP support. Within eligible fields of study, there are ineligible areas of study and ineligible areas of proposed research. See below for ineligible areas of study and proposed research. Applications determined to be ineligible will not be reviewed. a) Ineligible degree programs Individuals are not eligible to apply if they will be enrolled in a practice-oriented professional degree program such as medical, dental, law, and public health degrees at any time during the fellowship. Ineligible degree programs include, but are not limited to, MBA, MPH, MSW, JD, MD, DVM and DDS. Joint or combined professional degree-science programs (e.g., MD/PhD or JD/PhD) and dual professional degree-science programs are also not eligible. Individuals enrolled in a graduate degree program while on a leave of absence from a professional degree program or professional degree-graduate degree joint program are not eligible. b) Ineligible areas of study Individuals are not eligible to apply if they will be enrolled in graduate study focused on clinical practice, counseling, social work, patient-oriented research, epidemiological and medical behavioral studies, outcomes research, and health services research. Ineligible study includes pharmacologic, non-pharmacologic, and behavioral interventions for disease or disorder prevention, prophylaxis, diagnosis, therapy, or treatment. Research to provide evidence leading to a scientific basis for consideration of a change in health policy or standard of care is not eligible. Graduate study focused on community, public, or global health, or other population-based research including medical intervention trials is also not eligible. c) Ineligible proposed research (i) Research for which the goals are directly human disease- or health-related, including the etiology, diagnosis, and/or treatment of disease or disorder is not eligible for support. Research activities using animal models of disease, for developing or testing of drugs or other procedures for treatment of disease or disorder are not eligible. (ii) Research focused on basic questions in plant pathology are eligible, however, applied studies focused on maximizing production in agricultural plants or impacts on food safety, are not eligible. (iii) Research with implications that inform policy is eligible. Research with the expressed intent to influence, advocate for, or effect specific policy outcomes is not eligible. d) Limited exceptions to ineligible proposed research (i) Certain areas of bioengineering research directed at medical use are eligible. These include research projects in bioengineering to aid persons with disabilities, or to diagnose or treat human disease or disorder, provided they apply engineering principles to problems in medicine while primarily advancing engineering knowledge. Applicants planning to study and conduct research in these areas of bioengineering should select biomedical engineering as the field of study. (ii) Certain areas of materials research directed at development of materials for use in biological or biomedical systems are eligible, provided they are focused on furthering fundamental materials research. (iii) Certain areas of research with etiology-, diagnosis-, or treatment-related goals that advance fundamental knowledge in engineering, mathematical, physical, computer or information sciences, are eligible for support. Applicants are advised to consult a faculty member, academic advisor, mentor, or other advisor for guidance on preparation of their research plans, and selection of Major Fields of Study and subfields.

V. Application Preparation And Submission Instructions

Fellowship applications must be submitted online using the NSF Graduate Research Fellowship Program Application Module at https://www.fastlane.nsf.gov/grfp/Login.do according to the deadline corresponding with the Field of Study selected in the application .

Applications must be received by 5:00 p.m. local time as determined by the applicant’s mailing address provided in the application. Applications received after the Field of Study deadline will not be reviewed . Applications submitted to a Field of Study deadline not in alignment with the proposed research plan will not be reviewed.

All reference letters must be submitted online by the reference writers through the GRFP Application Module ( https://www.fastlane.nsf.gov/grfp/Login.do ) and must be received by the reference letter deadline (see Application Preparation and Submission Instructions/C. Due Dates of this Solicitation), of 5:00 p.m. Eastern Time (ET). Reference letter writers cannot be family members of the applicant. Applicants are required to provide the name and contact information for three (3) reference writers from non-family members. Up to five (5) potential reference letter writers can be provided. Two reference letters from non-family members must be received by the reference letter deadline applications to be reviewed. If fewer than two reference letters (one or none) are received by the reference letter deadline, the application will not be reviewed.

Applicants must submit the following information through the GRFP Application Module: Personal Information; Education, Work and Other Experience; Transcript PDFs; Proposed Field(s) of Study; Proposed Graduate Study and Graduate School Information; the names and email addresses of at least three reference letter writers; Personal, Relevant Background and Future Goals Statement PDF; and Graduate Research Plan Statement PDF.

Only the information required in the GRFP Application Module will be reviewed. No additional items or information will be accepted or reviewed. Do not provide links to web pages within the application, except as part of citations in the References Cited section. Images must be included in the page limits. Review of the application and reference letters is based solely on materials received by the application and reference letter deadlines. Do not email application materials.

Applicants must follow the instructions in the GRFP Application Module for completing each section of the application. The statements must be written using the following guidelines:

• standard 8.5" x 11" page size
• 11 point or higher font, except text that is part of an image
• Times New Roman font for all text, Cambria Math font for equations, Symbol font for non-alphabetic characters (it is recommended that equations and symbols be inserted as an image)
• 1" margins on all sides, no text inside 1" margins (no header, footer, name, or page number)
• No less than single-spacing (approximately 6 lines per inch)
• Do not use line spacing options such as “exactly 11 point,” that are less than single spaced
• PDF file format only

Compliance with these guidelines will be automatically checked by the GRFP Application Module. Documents that are not compliant will not be accepted by the GRFP Application Module. Applicants are strongly advised to proofread and upload their documents early to ensure they are format-compliant and that non-compliant documents do not delay upload of the complete application for receipt by the deadline. Applications that are not compliant with these format requirements will not be reviewed.

The maximum length of the Personal, Relevant Background and Future Goals Statement is three (3) pages (PDF). The maximum length of the Graduate Research Plan Statement is two (2) pages (PDF). These page limits include all references, citations, charts, figures, images, and lists of publications and presentations. Applicants must certify that the two statements (Personal, Relevant Background and Future Goals Statement, and Graduate Research Plan Statement) in the application are their own original work. As explained in the NSF Proposal and Award Policies and Procedures Guide (PAPPG): “NSF expects strict adherence to the rules of proper scholarship and attribution. The responsibility for proper scholarship and attribution rests with the authors of a proposal; all parts of the proposal should be prepared with equal care for this concern. Authors other than the PI (or any co-PI) should be named and acknowledged. Serious failure to adhere to such standards can result in findings of research misconduct. NSF policies and rules on research misconduct are discussed in the PAPPG, as well as 45 CFR Part 689."

Both statements must address NSF’s review criteria of Intellectual Merit and Broader Impacts (described in detail in Section VI). " Intellectual Merit" and "Broader Impacts" sections must be present under separate headings in both Personal and Research Plan statements. Applications that do not have separate headings for Intellectual Merit and Broader Impacts will not be reviewed.

In the application, applicants must list their undergraduate institution, and all graduate institutions attended with a start date prior to the fall term in which the application is submitted. Transcripts are required for all degree-granting programs listed. Transcripts may be included for all other institutions listed in the Education section. If the applicant started at the current institution in the fall of the application year and the institution does not provide unofficial or official transcripts prior to completion of the first term, the applicant may submit a class schedule/enrollment verification form in place of a transcript. At least one transcript must be included for the application to be accepted by the GRFP Application Module.

Transcripts must be uploaded through the GRFP Application Module by the Field of Study application deadline. Applicants should redact personally-identifiable information (date of birth, individual Social Security Numbers, personal financial information, home addresses, home telephone numbers and personal email addresses) from the transcripts before uploading. Transcripts must be uploaded as a PDF to be accepted by the GRFP Application Module. Transcripts must not be encrypted; the GRFP Application Module does not accept encrypted or password-protected transcripts.

Applicants who earned master’s degrees in joint Bachelor's-Master’s degree programs should submit transcripts that clearly document the joint program. If the transcript does not document the joint program and does not show that the Bachelor's and Master's degrees were conferred on the same date, applicants must upload a letter from the registrar of the institution certifying enrollment in a joint program, appended to the transcript for that institution. Failure to provide clear documentation of a joint program may result in an application being returned without review.

Failure to comply fully with the above requirements will result in the application not being reviewed.

Applications that are incomplete due to missing required transcripts and/or reference letters (fewer than two letters received), or that do not have "received" status in the Application Module on the application deadline for the selected Field of Study) will not be reviewed. Applicants are advised to submit applications early to avoid unanticipated delays on the deadline dates.

Reference Letters Reference writers cannot be family members of the applicant. Applicants are required to provide the name and contact information for three (3) reference writers from non-family members. Up to five (5) potential reference letter writers can be provided. Two reference letters from non-family members must be received by the reference letter deadline for an application to be reviewed. If fewer than two reference letters (one or none) are received by the reference letter deadline, the application will not be reviewed.

No changes to the list of reference writers are allowed after the application is submitted. Applicants are strongly advised to check the accuracy of email addresses provided for reference writers before submitting their application.

All reference letters must be received in the GRFP Application Module by 5:00 p.m. ET (Eastern Time) on the letter submission deadline date (see the deadline posted in GRFP Application Module and in Application Preparation and Submission Instructions/C. Due Dates of this Solicitation). No exceptions to the reference letter submission deadline will be granted. Each letter is limited to two (2) pages (PDF). The GRFP Application Module allows applicants to request up to five (5) reference letters and to rank those reference letters in order of preference for review. If more than three reference letters are received, the top three letters according to ranked preference will be considered for the application. Reference writers will be notified by an email of the request to submit a letter of reference on behalf of an applicant. Reference writers will not be notified of the ranked preference for review provided by the applicant.

To avoid disqualifying an application, reference writers should upload the letter well in advance of the 5:00 p.m. ET deadline . No letters will be accepted via email. Letter writers will receive a confirmation email after successful upload via the GRFP Application Module.

For technical assistance with letter upload: NSF Help Desk: [email protected] ; 1-800-381-1532

Applicants must enter an email address for each reference writer into the GRFP Application Module. An exact email address is crucial to matching the reference writer and the applicant in the GRFP Application Module. Applicants should ask reference writers well in advance of the reference writer deadline, and it is recommended they provide copies of their application materials to the writers.

Applicant-nominated reference writers must upload their letters through the GRFP Application Module. Reference letter requirements include:

• Institutional or professional letterhead, if available
• SIGNED by the reference writer, including the name, professional title, department, and institution
• Two (2) page limit (PDF file format)
• Standard 8.5" x 11" page size
• 11-point or higher Times New Roman font and 1" margins on all sides
• Single spaced using normal (100%) single-line spacing

The reference letter should address the NSF Merit Review Criteria of Intellectual Merit and Broader Impacts (described in detail below). It should include details explaining the nature of the relationship to the applicant (including research advisor role), comments on the applicant's potential for contributing to a globally-engaged United States science and engineering workforce, statements about the applicant's academic potential and prior research experiences, statements about the applicant's proposed research, and any other information to aid review panels in evaluating the application according to the NSF Merit Review Criteria.

Application Completion Status

Applicants should use the "Application Completion Status" feature in the GRFP Application Module to ensure all application materials, including reference letters, have been received by NSF before the deadlines. For technical support, call the NSF Help Desk at 1-800-381-1532 or e-mail [email protected] .

Interdisciplinary Applications

NSF welcomes applications for interdisciplinary programs of study and research; however, data on interdisciplinary study is collected for informational purposes only. Interdisciplinary research is defined as "a mode of research by teams or individuals that integrates information, data, techniques, tools, perspectives, concepts, and/or theories from two or more disciplines or bodies of specialized knowledge to advance fundamental understanding or to solve problems whose solutions are beyond the scope of a single discipline or area of research practice" (Committee on Facilitating Interdisciplinary Research, Committee on Science, Engineering, and Public Policy, 2004. Facilitating interdisciplinary research . National Academies. Washington: National Academy Press, p. 2). Applications must be received by the deadline for the first Major Field of Study designated in the application. Applications will be reviewed by experts in the first Major Field of Study listed. If awarded, Fellows will be required to enroll in a degree program consistent with the Major Field of Study in which the application was funded. Withdrawal of a GRFP application

To withdraw a submitted application, the applicant must withdraw their application using the Withdrawal option in the GRFP Application Module.

Applications withdrawn by November 15 of the application year do not count toward the one-time graduate application limit. Applications withdrawn after November 15 count toward this limit.

Cost Sharing:

Indirect Cost (F&A) Limitations:

NSF awards $53,000 each year to the GRFP institution to cover the Fellow stipend and Cost of Education allowance for each NSF Graduate Research Fellow "on tenure" at the institution.

The NSF Graduate Research Fellowship Program Fellowship stipend is $37,000 for a 12-month tenure period, prorated in monthly increments of $3,083. The institutional Cost of Education allowance is $16,000 per tenure year per Fellow.

D. Application Submission Requirements

Applicants are required to prepare and submit all applications for this program solicitation through the GRFP Application Module. Detailed instructions for application preparation and submission are available at: https://www.research.gov/grfp/Login.do . For user support, call the NSF Help Desk at 1-800-381-1532 or e-mail [email protected] . The NSF Help Desk answers general technical questions related to the use of the system. Specific questions related to this program solicitation should be referred to the NSF program staff contact(s) listed in Section VIII of this solicitation.

VI. Application Review Information

A. merit review principles and criteria.

Applications are reviewed by disciplinary and interdisciplinary scientists and engineers and other professional graduate education experts. Reviewers are selected by Program Officers charged with oversight of the review process. Care is taken to ensure that reviewers have no conflicts of interest with the applicants. Applications are reviewed in broad areas of related disciplines based on the selection of a Field of Study (see Fields of Study in Appendix). Selection of a Major Field of Study determines the application deadline, the broad disciplinary expertise of the reviewers, and the discipline of the graduate degree program if awarded a Fellowship. Applicants are advised to select the Major Field of Study in the GRFP Application Module (see Fields of Study in Appendix) that is most closely aligned with the proposed graduate program of study and research plan. Applicants who select “Other” must provide additional information describing their studies.

Each application will be reviewed independently in accordance with the NSF Merit Review Criteria using all available information in the completed application. In considering applications, reviewers are instructed to address the two Merit Review Criteria as approved by the National Science Board - Intellectual Merit and Broader Impacts ( NSF Proposal and Award Policies and Procedures Guide ). Applicants must include separate statements on Intellectual Merit and Broader Impacts in their written statements in order to provide reviewers with the information necessary to evaluate the application with respect to both Criteria as detailed below . Applicants should include headings for Intellectual Merit and Broader Impacts in their statements.

The following description of the Merit Review Criteria is provided in Chapter III of the NSF Proposal and Award Policies and Procedures Guide (PAPPG) :

All NSF proposals are evaluated through use of the two National Science Board approved merit review criteria. In some instances, however, NSF will employ additional criteria as required to highlight the specific objectives of certain programs and activities.

The two merit review criteria are listed below. Both criteria are to be given full consideration during the review and decision-making processes; each criterion is necessary but neither, by itself, is sufficient. Therefore, proposers must fully address both criteria. (PAPPG Chapter II.C.2.d.i. contains additional information for use by proposers in development of the Project Description section of the proposal.) Reviewers are strongly encouraged to review the criteria, including PAPPG Chapter II.C.2.d.i., prior to the review of a proposal.

When evaluating NSF proposals, reviewers will be asked to consider what the proposers want to do, why they want to do it, how they plan to do it, how they will know if they succeed, and what benefits could accrue if the project is successful. These issues apply both to the technical aspects of the proposal and the way in which the project may make broader contributions. To that end, reviewers will be asked to evaluate all proposals against two criteria:

• Intellectual Merit : The Intellectual Merit criterion encompasses the potential to advance knowledge; and
• Broader Impacts : The Broader Impacts criterion encompasses the potential to benefit society and contribute to the achievement of specific, desired societal outcomes.

The following elements should be considered in the review for both criteria:

1. What is the potential for the proposed activity to:

a. Advance knowledge and understanding within its own field or across different fields (Intellectual Merit); and

b. Benefit society or advance desired societal outcomes (Broader Impacts)?

2. To what extent do the proposed activities suggest and explore creative, original, or potentially transformative concepts?

3. Is the plan for carrying out the proposed activities well-reasoned, well-organized, and based on a sound rationale? Does the plan incorporate a mechanism to assess success?

4. How well qualified is the individual, team, or organization to conduct the proposed activities?

5. Are there adequate resources available to the PI (either at the home organization or through collaborations) to carry out the proposed activities?

Additionally, Chapter II of the NSF Proposal and Award Policies and Procedures Guide states:

Broader impacts may be accomplished through the research itself, through the activities that are directly related to specific research projects, or through activities that are supported by, but are complementary to, the project. NSF values the advancement of scientific knowledge and activities that contribute to achievement of societally relevant outcomes. Such outcomes include, but are not limited to: full participation of women, persons with disabilities, and underrepresented minorities in science, technology, engineering, and mathematics (STEM); improved STEM education and educator development at any level; increased public scientific literacy and public engagement with science and technology; improved well-being of individuals in society; development of a diverse, globally competitive STEM workforce; increased partnerships between academia, industry, and others; improved national security; increased economic competitiveness of the US; and enhanced infrastructure for research and education.

B. Application Review and Selection Process

Applications submitted in response to this program solicitation will be reviewed online by Panel Review.

The application evaluation involves the review and rating of applications by disciplinary and interdisciplinary scientists and engineers, and other professional graduate education experts.

Applicants are reviewed on their demonstrated potential to advance knowledge and to make significant research achievements and contributions to their fields throughout their careers. Reviewers are asked to assess applications using a holistic, comprehensive approach, giving balanced consideration to all components of the application, including the educational and research record, leadership, outreach, service activities, and future plans, as well as individual competencies, experiences, and other attributes. The aim is to recruit and retain a diverse cohort of early-career individuals with high potential for future achievements, contributions, and broader impacts in STEM and STEM education.

The primary responsibility of each reviewer is to evaluate eligible GRFP applications by applying the Merit Review Criteria described in Section VI.A, and to recommend applicants for NSF Graduate Research Fellowships. Reviewers are instructed to review the applications holistically, applying the Merit Review Criteria and noting GRFP’s emphasis on demonstrated potential for significant research achievements in STEM or in STEM education. From these recommendations, NSF selects applicants for Fellowships or Honorable Mention, in line with NSF’s mission and the goals of GRFP. After Fellowship offers are made, applicants are able to view verbatim reviewer comments, excluding the names of the reviewers, for a limited period of time through the NSF GRFP Module.

VII. Award Administration Information

A. notification of the award.

NSF Graduate Research Fellowship Program applicants will be notified of the outcomes of their applications by early April of the competition year. The NSF publishes lists of Fellowship and Honorable Mention recipients on the GRFP Module at https://www.research.gov/grfp/Login.do in early April.

B. Award Conditions

NSF GRFP awards are made to the institution of higher education at which a Fellow is or will be enrolled. The awardee institution is responsible for financial management of the award and disbursement of Fellowship funds to the Fellow. The NSF GRFP award consists of the award notification letter that includes the applicable terms and conditions and Fellowship management instructions. All Fellowships are made subject to the provisions (and any subsequent amendments) contained in the document NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials .

NSF GRFP awards provide funds for NSF Fellows who have "on tenure" status. The institution will administer the awards, including any amendments, in accordance with the terms of the Agreement and provisions (and any subsequent amendments) contained in the document NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials .

The applicant must accept or decline the Fellowship by the deadline indicated in the award notification letter by logging into the GRFP Module at https://www.research.gov/grfp/Login.do with the applicant User ID and password. Failure to comply with the deadline and acceptance of Fellowship Terms and Conditions by the deadline will result in revocation of the Fellowship offer and render applicants ineligible to re-apply.

Terms and Conditions

Awardees must formally accept and agree to the terms and conditions of the Fellowship award. Acceptance of the Fellowship constitutes a commitment to pursue a graduate degree in an eligible science or engineering field. Acceptance of a Fellowship award is an explicit acceptance of this commitment and assurance that the Fellow will be duly enrolled in a graduate degree program consistent with the field of study indicated in their application by the beginning of the following academic year. Major changes in scope later in the graduate career require NSF approval. NSF Graduate Research Fellowship Program Administrative Guide for Fellows and Coordinating Officials includes the terms and conditions that apply to the Fellowship and subsequent institutional award, in addition to the eligibility requirements (U.S. citizen, national, or permanent resident, degree requirements, and field of study) and Certifications in the application. Each institution, in accepting the funds, also certifies that the Fellows are eligible to receive the Fellowship under these terms and conditions. Fellows are expected to make satisfactory academic progress towards completion of their graduate degrees, as defined and certified by the Fellow's GRFP institution. In cases where Fellows have misrepresented their eligibility, or have failed to comply with the Fellowship Terms and Conditions, the Fellowship will be revoked, and the case may be referred to the Office of the Inspector General for investigation. This action may result in requiring the Fellow to repay Fellowship funds to the National Science Foundation.

An individual may not accept the Graduate Research Fellowship if the individual accepts or is supported by another federal graduate fellowship.

Responsible Conduct of Research

It is the responsibility of the Fellow, in conjunction with the GRFP institution, to ensure that all academic and research activities carried out in or outside the US comply with the laws or regulations of the US and/or of the foreign country in which the academic and/or research activities are conducted. These include appropriate human subject, animal welfare, copyright and intellectual property protection, and other regulations or laws, as appropriate. All academic and research activities should be coordinated with the appropriate US and foreign government authorities, and necessary licenses, permits, or approvals must be obtained prior to undertaking the proposed activities.

In response to the America COMPETES Act, all Fellows supported by NSF to conduct research are required to receive appropriate training and oversight in the Responsible and Ethical Conduct of Research.

Research Involving Human Subjects

Projects involving research with human subjects must ensure that subjects are protected from research risks in conformance with the relevant Federal policy known as the Common Rule ( Federal Policy for the Protection of Human Subjects , 45 CFR 690 ). All projects involving human subjects must either (1) have approval from an Institutional Review Board (IRB) before issuance of an NSF award; or, (2) must affirm that the IRB has declared the research exempt from IRB review, in accordance with the applicable subsection, as established in 45 CFR § 690.104(d) of the Common Rule. Fellows are required to comply with this policy and adhere to the organization's protocol for managing research involving human subjects.

Research Involving Vertebrate Animals

Any project proposing use of vertebrate animals for research or education shall comply with the Animal Welfare Act [7 U.S.C. 2131 et seq.] and the regulations promulgated thereunder by the Secretary of Agriculture [9 CFR 1.1-4.11] pertaining to the humane care, handling, and treatment of vertebrate animals held or used for research, teaching or other activities supported by Federal awards. In accordance with these requirements, proposed projects involving use of any vertebrate animal for research or education must be approved by the submitting organization's Institutional Animal Care and Use Committee (IACUC) before an award can be made. For this approval to be accepted by NSF, the organization must have a current Public Health Service (PHS) Approved Assurance.

Projects involving the care or use of vertebrate animals at an international organization or international field site also require approval of research protocols by the US grantee’s IACUC. If the project is to be funded through an award to an international organization or through an individual fellowship award that will support activities at an international organization, NSF will require a statement from the international organization explicitly listing the proposer’s name and referencing the title of the award to confirm that the activities will be conducted in accordance with all applicable laws in the international country and that the International Guiding Principles for Biomedical Research Involving Animals (see: http://www.cioms.ch/ ) will be followed.

Legal Rights to Intellectual Property

The National Science Foundation claims no rights to any inventions or writings that might result from its fellowship or traineeship grants. However, fellows and trainees should be aware that the NSF, another Federal agency, or some private party may acquire such rights through other support for particular research. Also, fellows and trainees should note their obligation to include an Acknowledgment and Disclaimer in any publication.

C. Reporting Requirements

Acknowledgment of Support and Disclaimer

All publications, presentations, and creative works based on activities conducted during the Fellowship must acknowledge NSF GRFP Support and provide a disclaimer by including the following statement in the Acknowledgements or other appropriate section:

"This material is based upon work supported by the National Science Foundation Graduate Research Fellowship Program under Grant No. (NSF grant number). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation."

Annual Activities Report and Annual Fellowship Status Declaration

Fellows are required to submit an Annual Activities Report and to complete Fellowship Status Declaration by the deadline date each year (deadline notification sent by email), using NSF's GRFP Module. The GRFP Module permits online submission and updating of activity reports, including information on research accomplishments and activities related to broader impacts, presentations, publications, teaching and research assistantships, awards and recognitions, and other scholarly and service accomplishments. These reports must be reviewed and satisfactory progress verified by the faculty advisor or designated graduate program administrator prior to submission to NSF.

Fellows must declare their intent to utilize the Fellowship for the following year using the NSF GRFP Module. Failure to declare Fellowship status by the established deadline violates the terms and conditions for NSF Fellowship awards, and results in termination of the Fellowship.

Program Evaluation

The Division of Graduate Education (DGE) conducts evaluations to provide evidence on the impact of the GRFP on individuals' educational decisions, career preparations, aspirations and progress, as well as professional productivity; and provide an understanding of the program policies in achieving the program goals. Additionally, it is highly desirable to have a structured means of tracking Fellows beyond graduation to gauge the extent to which they choose a career path consistent with the intent of the program and to assess the impact the NSF Graduate Research Fellowship has had on their graduate education experience. Accordingly, Fellows and Honorable Mention recipients may be contacted for updates on various aspects of their employment history, professional activities and accomplishments, participation in international research collaborations, and other information helpful in evaluating the impact of the program. Fellows and their institutions agree to cooperate in program-level evaluations conducted by the NSF and/or contracted evaluators. The 2014 GRFP evaluation is posted on the "Evaluation Reports" Web page for NSF's Directorate for STEM Education.

GRFP institutions are required to submit the GRFP Completion Report annually. The Completion Report allows GRFP institutions to certify the current status of all GRFP Fellows at the institution. The current status will identify a Fellow as: In Progress, Graduated, Transferred, or Withdrawn. For Fellows who have graduated, the graduation date is a required reporting element.

VIII. Agency Contacts

Please note that the program contact information is current at the time of publishing. See program website ( https://www.nsf.gov/funding/pgm_summ.jsp?pims_id=6201 ) for any updates to the points of contact.

General inquiries regarding this program should be made to:

For questions related to the use of GRFP Application Module, contact:

NSF Help Desk: telephone: 1-800-381-1532; e-mail: [email protected]

The Graduate Research Fellowship Operations Center is responsible for processing applications and responding to requests for information. General inquiries regarding the Graduate Research Fellowship Program should be made to:

Graduate Research Fellowship Operations Center, telephone: 866-NSF-GRFP, 866-673-4737 (toll-free from the US and Canada) or 202-331-3542 (international). email: [email protected]

IX. Other Information

The NSF website provides the most comprehensive source of information on NSF Directorates (including contact information), programs and funding opportunities. Use of this website by potential proposers is strongly encouraged. In addition, "NSF Update" is an information-delivery system designed to keep potential proposers and other interested parties apprised of new NSF funding opportunities and publications, important changes in proposal and award policies and procedures, and upcoming NSF Grants Conferences . Subscribers are informed through e-mail or the user's Web browser each time new publications are issued that match their identified interests. "NSF Update" also is available on NSF's website .

Grants.gov provides an additional electronic capability to search for Federal government-wide grant opportunities. NSF funding opportunities may be accessed via this mechanism. Further information on Grants.gov may be obtained at https://www.grants.gov .

Students are encouraged to gain professional experience in other countries through their university graduate programs, and to participate in international research opportunities offered by NSF at: Office of International Science and Engineering (OISE) | NSF - National Science Foundation . Other funding opportunities for students are available at http://www.nsfgrfp.org/ .

About The National Science Foundation

The National Science Foundation (NSF) is an independent Federal agency created by the National Science Foundation Act of 1950, as amended (42 USC 1861-75). The Act states the purpose of the NSF is "to promote the progress of science; [and] to advance the national health, prosperity, and welfare by supporting research and education in all fields of science and engineering."

NSF funds research and education in most fields of science and engineering. It does this through grants and cooperative agreements to more than 2,000 colleges, universities, K-12 school systems, businesses, informal science organizations and other research organizations throughout the US. The Foundation accounts for about one-fourth of Federal support to academic institutions for basic research.

NSF receives approximately 55,000 proposals each year for research, education and training projects, of which approximately 11,000 are funded. In addition, the Foundation receives several thousand applications for graduate and postdoctoral fellowships. The agency operates no laboratories itself but does support National Research Centers, user facilities, certain oceanographic vessels and Arctic and Antarctic research stations. The Foundation also supports cooperative research between universities and industry, US participation in international scientific and engineering efforts, and educational activities at every academic level.

Facilitation Awards for Scientists and Engineers with Disabilities (FASED) provide funding for special assistance or equipment to enable persons with disabilities to work on NSF-supported projects. See the NSF Proposal & Award Policies & Procedures Guide Chapter II.F.7 for instructions regarding preparation of these types of proposals.

The National Science Foundation has Telephonic Device for the Deaf (TDD) and Federal Information Relay Service (FIRS) capabilities that enable individuals with hearing impairments to communicate with the Foundation about NSF programs, employment or general information. TDD may be accessed at (703) 292-5090 and (800) 281-8749, FIRS at (800) 877-8339.

The National Science Foundation Information Center may be reached at (703) 292-5111.

The National Science Foundation promotes and advances scientific progress in the United States by competitively awarding grants and cooperative agreements for research and education in the sciences, mathematics, and engineering. To get the latest information about program deadlines, to download copies of NSF publications, and to access abstracts of awards, visit the NSF Website at 2415 Eisenhower Avenue, Alexandria, VA 22314 (NSF Information Center) (703) 292-5111 (703) 292-5090 Send an e-mail to: or telephone: (703) 292-8134 (703) 292-5111 Privacy Act And Public Burden Statements The information requested on the application materials is solicited under the authority of the National Science Foundation Act of 1950, as amended. It will be used in connection with the selection of qualified applicants and may be disclosed to qualified reviewers as part of the review process; to the institution the nominee, applicant or fellow is attending or is planning to attend or is employed by for the purpose of facilitating review or award decisions, or administering fellowships or awards; to government contractors, experts, volunteers and other individuals who perform a service to or work under a contract, grant, cooperative agreement, advisory committee, committee of visitors, or other arrangement with the Federal government as necessary to complete assigned work; to other government agencies needing data regarding applicants or nominees as part of the review process, or in order to coordinate programs; and to another Federal agency, court or party in a court or Federal administrative proceeding if the government is a party. Information from this system may be merged with other computer files to carry out statistical studies the results of which do not identify individuals. Notice of the agency's decision may be given to nominators, and disclosure may be made of awardees' names, home institutions, and fields of study for public information purposes. For fellows or awardees receiving stipends directly from the government, information is transmitted to the Department of the Treasury to make payments. See System of Record Notices , NSF-12, "Fellowships and Other Awards," 63 Federal Register 265 (January 5, 1998). Submission of the information is voluntary; however, failure to provide full and complete information may reduce the possibility of your receiving an award. An agency may not conduct or sponsor, and a person is not required to respond to, an information collection unless it displays a valid Office of Management and Budget (OMB) control number. The OMB control number for this collection is 3145-0023. Public reporting burden for this collection of information is estimated to average 12 hours per response, including the time for reviewing instructions. Send comments regarding this burden estimate and any other aspect of this collection of information, including suggestions for reducing this burden, to: Suzanne H. Plimpton Reports Clearance Officer Policy Office, Division of Institution and Award Support Office of Budget, Finance, and Award Management National Science Foundation Alexandria, VA 22314 X. Appendix NATIONAL SCIENCE FOUNDATION GRADUATE RESEARCH FELLOWSHIPS Major Fields of Study Note: Applications are reviewed based on the selection of a Major Field of Study. As an example, CHEMISTRY is a Major Field of Study, and Chemical Catalysis is a subfield under CHEMISTRY. Selection of a Major Field of Study determines the application deadline, the broad disciplinary expertise of the reviewers who will review the application, and the discipline of the graduate program if the Fellowship is accepted. The subfield category designates specific expertise of the reviewers. Applicants can select “Other” if their specific subfield is not represented in the list of subfields under the Major Field of Study. The "Other" subfield category should be selected only if the proposed subfield is not covered by one of the listed subfields, and should not be used to designate a subfield that is more specific than the subfields listed. Artificial Intelligence Chemical Catalysis Chemical Measurement and Imaging Chemical Structure, Dynamics, and Mechanism Chemical Synthesis Chemical Theory, Models and Computational Methods Chemistry of Life Processes Computationally Intensive Research Environmental Chemical Systems Macromolecular, Supramolecular, and Nanochemistry Other (specify) Quantum Information Science Sustainable Chemistry COMPUTER AND INFORMATION SCIENCES & ENGINEERING Accessibility Algorithms and Theoretical Foundations Artificial Intelligence Augmented Reality/Virtual Reality, Graphics, and Visualization Bioinformatics and Bio-inspired Computing Communication and Information Theory Computationally Intensive Research Computer Architecture Computer Security and Privacy Computer Systems Computer Vision Cyber-Physical Systems and Embedded Systems Data Science, Data Mining, Information Retrieval and Databases Electronic Design Automation and Design of Micro and Nano Computing Systems Fairness, Explainability, Accountability and Transparency in Analytics Formal Methods, Verification, and Programming Languages Human Computer Interaction Information Sciences Machine Learning Natural Language Processing Other (specify) Parallel, Distributed, and Cloud Computing Quantum Information Science Robotics Scientific Computing Social Computing Software Engineering Wired and Wireless Networking ENGINEERING Aeronautical and Aerospace Engineering Artificial Intelligence Bioengineering Biomedical Engineering Chemical Engineering Civil Engineering Computationally Intensive Research Computer Engineering Electrical and Electronic Engineering Energy Engineering Environmental Engineering Industrial Engineering & Operations Research Manufacturing Engineering Materials Engineering Mechanical Engineering Nuclear Engineering Ocean Engineering Optical Engineering Other (specify) Quantum Engineering Quantum Information Science Systems Engineering Wireless Engineering GEOSCIENCES Aeronomy Artificial Intelligence Arctic-Antarctic Atmospheric Chemistry Biogeochemistry Biological Oceanography Chemical Oceanography Climate and Large-Scale Atmospheric Dynamics Computationally Intensive Research Geobiology Geochemistry Geodynamics Geomorphology Geophysics Glaciology Hydrology Magnetospheric Physics Marine Biology Marine Geology and Geophysics Other (specify) Paleoclimate Paleontology and Paleobiology Petrology Physical and Dynamic Meteorology Physical Oceanography Quantum Information Science Sedimentary Geology Solar Physics Tectonics LIFE SCIENCES Artificial Intelligence Biochemistry Bioinformatics and Computational Biology Biophysics Cell Biology Computationally Intensive Research Developmental Biology Ecology Environmental Biology Evolutionary Biology Genetics Genomics Microbial Biology Neurosciences Organismal Biology Other (specify) Physiology Proteomics Quantum Information Science Structural Biology Systematics and Biodiversity Systems and Molecular Biology MATERIALS RESEARCH Artificial Intelligence Biomaterials Ceramics Chemistry of Materials Computationally Intensive Research Electronic Materials Materials Theory Metallic Materials Other (specify) Photonic Materials Physics of Materials Polymers Quantum Information Science MATHEMATICAL SCIENCES Algebra, Number Theory, and Combinatorics Analysis Applied Mathematics Artificial Intelligence Biostatistics Computational and Data-enabled Science Computational Mathematics Computational Statistics Computationally Intensive Research Geometric Analysis Logic or Foundations of Mathematics Mathematical Biology Other (specify) Probability Quantum Information Science Statistics Topology PHYSICS & ASTRONOMY Artificial Intelligence Astronomy and Astrophysics Atomic, Molecular and Optical Physics Computationally Intensive Research Condensed Matter Physics Nuclear Physics Other (specify) Particle Physics Physics of Living Systems Plasma Physics Quantum Information Science Solid State Physics Theoretical Physics Artificial Intelligence Cognitive Neuroscience Cognitive Psychology Comparative Psychology Computational Psychology Computationally Intensive Research Developmental Psychology Industrial/Organizational Psychology Neuropsychology Other (specify) Perception and Psychophysics Personality and Individual Differences Physiological Psychology Psycholinguistics Quantitative Psychology Quantum Information Science Social/Affective Neuroscience Social Psychology SOCIAL SCIENCES Anthropology, other (specify) Archaeology Artificial Intelligence Biological Anthropology Communications Computationally Intensive Research Cultural Anthropology Decision Making and Risk Analysis Economics Geography History and Philosophy of Science International Relations Law and Social Science Linguistic Anthropology Linguistics Medical Anthropology Other (specify) Political Science Public Policy Quantum Information Science Science Policy Sociology Urban and Regional Planning STEM EDUCATION AND LEARNING RESEARCH Artificial Intelligence Computationally Intensive Research Engineering Education Mathematics Education Other (specify) Quantum Information Science Science Education Technology Education News & Events International Conference: "Language, Education and Artificial Intelligence" News and Blog International conference: “language, education and artificial intelligence”. The School of Education Sciences of the University of Crete, the Institute of Greek Language of the University of Western Macedonia, and the Postgraduate Program ‘Education Sciences’ (Specialization ‘Language, Society, and Education’) of the Hellenic Open University are co-organizing an  International Conference  on the theme:  ‘Language, Education, and Artificial Intelligence’ . The conference will be held in Rethymno from the  10th  to the  12th of May 2024 . Please find the invitation and more information here Conference poster   here Conference website:   https://le-ai.edc.uoc.gr/en/ Conference email:     le- [email protected] International open call for expression of interest for the nomination of five external members of the University of Crete Ingenium joint research group proposals, related posts. The Rector Welcomes New Students University of Crete Launches E-Stories Digital Storytelling Course for international students University of crete’s summer programs 2024, enjoy your summer, uoc news international #10 – university of crete. International relations (3) News & Events (349) Newsletter (1) Uncategorized (4) Popular tags General Information Career and Liaison Office Students’ Counceling Center Phone Directory Research Directory Userful Links Contact Welcome Office Visit UOC Copyright © 2024 – Development & Supported by D.G.U. Accessibility Accessibility modes, online dictionary, readable experience, visually pleasing experience, easy orientation. University of Crete Accessibility Statement Accessibility Statement September 10, 2024 Compliance status We firmly believe that the internet should be available and accessible to anyone, and are committed to providing a website that is accessible to the widest possible audience, regardless of circumstance and ability. To fulfill this, we aim to adhere as strictly as possible to the World Wide Web Consortium’s (W3C) Web Content Accessibility Guidelines 2.1 (WCAG 2.1) at the AA level. These guidelines explain how to make web content accessible to people with a wide array of disabilities. Complying with those guidelines helps us ensure that the website is accessible to all people: blind people, people with motor impairments, visual impairment, cognitive disabilities, and more. This website utilizes various technologies that are meant to make it as accessible as possible at all times. We utilize an accessibility interface that allows persons with specific disabilities to adjust the website’s UI (user interface) and design it to their personal needs. Additionally, the website utilizes an AI-based application that runs in the background and optimizes its accessibility level constantly. This application remediates the website’s HTML, adapts Its functionality and behavior for screen-readers used by the blind users, and for keyboard functions used by individuals with motor impairments. If you’ve found a malfunction or have ideas for improvement, we’ll be happy to hear from you. You can reach out to the website’s operators by using the following email Screen-reader and keyboard navigation Our website implements the ARIA attributes (Accessible Rich Internet Applications) technique, alongside various different behavioral changes, to ensure blind users visiting with screen-readers are able to read, comprehend, and enjoy the website’s functions. As soon as a user with a screen-reader enters your site, they immediately receive a prompt to enter the Screen-Reader Profile so they can browse and operate your site effectively. Here’s how our website covers some of the most important screen-reader requirements, alongside console screenshots of code examples: Screen-reader optimization: we run a background process that learns the website’s components from top to bottom, to ensure ongoing compliance even when updating the website. In this process, we provide screen-readers with meaningful data using the ARIA set of attributes. For example, we provide accurate form labels; descriptions for actionable icons (social media icons, search icons, cart icons, etc.); validation guidance for form inputs; element roles such as buttons, menus, modal dialogues (popups), and others. Additionally, the background process scans all the website’s images and provides an accurate and meaningful image-object-recognition-based description as an ALT (alternate text) tag for images that are not described. It will also extract texts that are embedded within the image, using an OCR (optical character recognition) technology. To turn on screen-reader adjustments at any time, users need only to press the Alt+1 keyboard combination. Screen-reader users also get automatic announcements to turn the Screen-reader mode on as soon as they enter the website. These adjustments are compatible with all popular screen readers, including JAWS and NVDA. Keyboard navigation optimization: The background process also adjusts the website’s HTML, and adds various behaviors using JavaScript code to make the website operable by the keyboard. This includes the ability to navigate the website using the Tab and Shift+Tab keys, operate dropdowns with the arrow keys, close them with Esc, trigger buttons and links using the Enter key, navigate between radio and checkbox elements using the arrow keys, and fill them in with the Spacebar or Enter key.Additionally, keyboard users will find quick-navigation and content-skip menus, available at any time by clicking Alt+1, or as the first elements of the site while navigating with the keyboard. The background process also handles triggered popups by moving the keyboard focus towards them as soon as they appear, and not allow the focus drift outside it. Users can also use shortcuts such as “M” (menus), “H” (headings), “F” (forms), “B” (buttons), and “G” (graphics) to jump to specific elements. Disability profiles supported in our website Epilepsy Safe Mode: this profile enables people with epilepsy to use the website safely by eliminating the risk of seizures that result from flashing or blinking animations and risky color combinations. Visually Impaired Mode: this mode adjusts the website for the convenience of users with visual impairments such as Degrading Eyesight, Tunnel Vision, Cataract, Glaucoma, and others. Cognitive Disability Mode: this mode provides different assistive options to help users with cognitive impairments such as Dyslexia, Autism, CVA, and others, to focus on the essential elements of the website more easily. ADHD Friendly Mode: this mode helps users with ADHD and Neurodevelopmental disorders to read, browse, and focus on the main website elements more easily while significantly reducing distractions. Blindness Mode: this mode configures the website to be compatible with screen-readers such as JAWS, NVDA, VoiceOver, and TalkBack. A screen-reader is software for blind users that is installed on a computer and smartphone, and websites must be compatible with it. Keyboard Navigation Profile (Motor-Impaired): this profile enables motor-impaired persons to operate the website using the keyboard Tab, Shift+Tab, and the Enter keys. Users can also use shortcuts such as “M” (menus), “H” (headings), “F” (forms), “B” (buttons), and “G” (graphics) to jump to specific elements. Additional UI, design, and readability adjustments Font adjustments – users, can increase and decrease its size, change its family (type), adjust the spacing, alignment, line height, and more. Color adjustments – users can select various color contrast profiles such as light, dark, inverted, and monochrome. Additionally, users can swap color schemes of titles, texts, and backgrounds, with over seven different coloring options. Animations – person with epilepsy can stop all running animations with the click of a button. Animations controlled by the interface include videos, GIFs, and CSS flashing transitions. Content highlighting – users can choose to emphasize important elements such as links and titles. They can also choose to highlight focused or hovered elements only. Audio muting – users with hearing devices may experience headaches or other issues due to automatic audio playing. This option lets users mute the entire website instantly. Cognitive disorders – we utilize a search engine that is linked to Wikipedia and Wiktionary, allowing people with cognitive disorders to decipher meanings of phrases, initials, slang, and others. Additional functions – we provide users the option to change cursor color and size, use a printing mode, enable a virtual keyboard, and many other functions. Browser and assistive technology compatibility We aim to support the widest array of browsers and assistive technologies as possible, so our users can choose the best fitting tools for them, with as few limitations as possible. Therefore, we have worked very hard to be able to support all major systems that comprise over 95% of the user market share including Google Chrome, Mozilla Firefox, Apple Safari, Opera and Microsoft Edge, JAWS and NVDA (screen readers). Notes, comments, and feedback Despite our very best efforts to allow anybody to adjust the website to their needs. There may still be pages or sections that are not fully accessible, are in the process of becoming accessible, or are lacking an adequate technological solution to make them accessible. Still, we are continually improving our accessibility, adding, updating and improving its options and features, and developing and adopting new technologies. All this is meant to reach the optimal level of accessibility, following technological advancements. For any assistance, please reach out to Hide similarities Highlight differences Availability Add to cart Description Additional information 451 IMAGES Innovative High Quality Artificial Intelligence Thesis Proposals Guidance Research Proposal Artificial Intelligence 28 High Quality Research Proposal on Artificial Intelligence [Novel] sample-artificial-intelligence2 PhD Services Artificial Intelligence Research Proposal (Paper Writing Guidance) VIDEO How do I write my PhD thesis about Artificial Intelligence, Machine Learning and Robust Clustering? Northwestern Medicine Healthcare AI Forum -- Feb 23, 2024 Understanding Data, Project Graduate Admission Prediction Using Machine Learning Tuning the Resonant Frequency of Microstrip Patch Antenna in LWIR 'Universal basic income' proposal from the father of artificial intelligence Research Proposal for PhD admission #profdrrajasekaran COMMENTS PDF PhD Proposal in Artificial Intelligence and Machine Learning PhD Proposal in Artificial Intelligence and Machine Learning PDF Phd Proposal in Artificial Intelligence and Machine Learning RESEARCH TOPIC. Generative Adversarial Networks (GANs) are a class of unsupervised machine learning techniques to estimate a distribution from high-dimensional data and to sample elements that mimic the observations (Goodfellow et al., 2014). They use a zero-sum dynamic game be- tween two neural networks: a generator, which generates new ... PDF Ph.D. proposal: Distributed Artificial Intelligence Integrated Circuits Ph.D. proposal: Distributed Artificial Intelligence Integrated ... Artificial Intelligence Research Proposal Artificial Intelligence Research Proposal PDF PhD proposal: An Artificial Intelligence framework to improve the The research domain of this PhD thesis is the formal modelling and analysis of complex dynamical systems (specifically in biological systems). Such a topic is the area of expertise of the MeForBio team (acronym for "Formal Methods for Bioinformatics") of LS2N, one of France's leading public research labs in digital sciences. The MeForBio ... Artificial Intelligence PhD Research Projects PhD Projects ... You haven't completed your profile yet. To get the most out of FindAPhD, finish your profile and receive these benefits: Monthly chance to win one of ten £10 Amazon vouchers; winners will be notified every month.*; The latest PhD projects delivered straight to your inbox; Access to our £6,000 scholarship competition; Weekly newsletter with funding opportunities, research proposal tips and ... Artificial Intelligence Research Topics for PhD Manuscripts 2021 Here some of the recent Research Topics, Artificial Intelligence and Machine learning - Recent Trands. How AI and ML can aid healthcare systems in their response to COVID-19. Machine learning and artificial intelligence in haematology. Tackling the risk of stranded electricity assets with machine learning and artificial intelligence. PDF Phd Proposal in Artificial Intelligence and Machine Learning ANITI - ARTIFICIAL & NATURAL INTELLIGENCE TOULOUSE INSTITUTE https://aniti.univ-toulouse.fr/ PHD PROPOSAL IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING DESCRIPTION In combinatorial optimization, the objective is to find an assignment of a set of decision variables that satisfy a set of constraints and that optimizes a given objective function. PhD Research Proposal Artificial Intelligence Need to be related with your degree and present research areas of artificial intelligence. In general, the PhD research proposal has a standard format to write. As well, it is composed of different components such as title, abstract, introduction, literature study, methodologies, conclusion, and references. In fact, we have a native writer team ... PhD in Artificial Intelligence CSCI/PHIL 6550 Artificial Intelligence (3 hours) ARTI 6950 Faculty Research Seminar (1 hour) ARTI/PHIL 6340 Ethics and Artificial Intelligence (3 hours) Elective Courses. In addition to the required courses above, at least 6 additional courses must be taken from Groups A and Group B below, subject to the following requirements. Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD Artificial Intelligence Enabled Healthcare MRes + MPhil/PhD artificial intelligence PhD Projects, Programmes & Scholarships Development of A Next-Generation Multimodal Artificial Intelligence Platform for Early Breast Cancer Diagnosis. Queen Mary University of London Barts and The London School of Medicine and Dentistry. This Barts Charity funded project will commence in January 2025 and has funding for 4 years. The student will be based at the Barts Cancer ... Phd Proposal in Artificial Intelligence and Machine Learning PHD PROPOSAL IN ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING. C. Pagetti. Published 2019. Computer Science. TLDR. Two PhD positions are available in the framework of the Horizon EU Project TUPLES : Trustworthy Planning and Scheduling with Learning and Explanations, aiming to develop scalable, yet transparent, robust and safe algorithmic ... Research Proposal: The impact of AI on the development of critical The impact of AI on the development of critical thinking and ... PhD on artificial intelligence for renewable energy and sustainability Fully-funded PhD in the area of artificial intelligence for renewable energy and sustainability. Start date 1 April 2023. Duration 3.5 years. ... In place of a research proposal you should upload a document stating the title of the project that you wish to apply for and the name of the relevant supervisor. Artificial Intelligence PhD Projects, Programmes & Scholarships A PhD in Artificial Intelligence is designed to further your research in the field of AI. Research in Artificial Intelligence seeks to understand how technology can be applied to improve the lives of humans. Doing a PhD in Artificial Intelligence, you'll be developing new technology that helps AI systems solve problems and make decisions. PDF PhD Project Proposals PhD Project Proposals Research Plan Public Defense The academic year of the second enrolment, each student must do a public presentation of his/her Research plan. The presentation will be evaluated by a three members jury chosen by the Academic Committee. The presentation consists of exposing the general goals proposed for the thesis, its justification according to the state of art in the research area, and the exposition of the way the main ... Artificial Intelligence and Data Analytics (AIDA) Group Suggested PhD Projects. Here are some suggested topics for PhD projects from our group members. These projects are merely suggestions and illustrate the broad interests of the research group. Potential research students are encouraged and welcome to produce their own suggestions in these research areas or other research areas in the field. PhD Artificial Intelligence Please note: We are only accepting applications for PhD in Artificial Intelligence through the Centre for Doctoral Training (CDT) in AI for Decision Making in Complex Systems. ... the CDT will be grounded in the research areas of physics and astronomy, engineering, biology, and material science, as well as a cross-cutting theme of using AI to ... Research Proposal to Specialists in Artificial Intelligence and Research Gate notification of 5 recommendations achieved by Research proposal: "Artificial Intelligence for Heavy Vehicle Technology: Subtextual AI/HVT Imagery in The Long, Long Trailer," by Nancy ... Artificial Intelligence (cyber security) PhD Research ... Search Funded PhD Research Projects in Computer Science, Artificial Intelligence, cyber security. Search for PhD funding, scholarships & studentships in the UK, Europe and around the world. PhDs ; ... PhD thesis PhD interview questions PhD research proposal Contacting potential PhD supervisors PhD blog Our editorial team View all advice guides. Research Proposal Topics and Ideas in Artificial Intelligence This is the first step in writing a research proposal here we find a problem of the proposed field of artificial intelligence that you want to address. It can be based on a specified challenge or limitation in AI algorithms, a demand for a more well-organized AI systems, or a longing to reconsider the ethical implications of AI technology. Smart Health and Biomedical Research in the Era of Artificial Full Proposal Deadline(s) (due by 5 p.m. submitting organization's local time): November 09, 2023. October 03, 2024. October 3, Annually Thereafter. Important Information And Revision Notes. The Smart Health program solicitation has been revised and prospective Principal Investigators (PIs) are encouraged to read the solicitation carefully. NSF 23-605: Graduate Research Fellowship Program (GRFP) Graduate Research Fellowship Program (GRFP) International Conference: "Language, Education and Artificial Intelligence" The School of Education Sciences of the University of Crete, the Institute of Greek Language of the University of Western Macedonia, and the Postgraduate Program 'Education Sciences' (Specialization 'Language, Society, and Education') of the Hellenic Open University are co-organizing an International Conference on the theme: 'Language, Education, and Artificial Intelligence'. assignment essay homework paper phd report resume review speech thesis