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Siemens opens its largest global research hub north of Munich

The research center stands for openness and for working in a research and innovation ecosystem, main areas of research, for this press release.

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Bernhard Wardin

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Postdoctoral Scientist (m/f/d) - supported by the Ferenc Krausz Fellowship 

Job Code: 2024-15

In a joint effort, the Max Planck Institute for Quantum Optics , the Ludwig-Maximilians University Munich  and the Center for Molecular Fingerprinting Research (CMF) combine cutting-edge femtosecond laser technologies with novel electric-field level techniques to advance a new type of mid-infrared spectroscopy. The unique systems are developed and utilized by a highly interdisciplinary team of physicists, data scientists, biologists and clinical personal to identify, via minuscule variations in the infrared response of human blood samples, medical conditions such as cancer.

Applications are invited for a two-year postdoctoral position in the field of Ultrafast filed-resolved infrared spectroscopy with enhanced detection.

The successful candidate will work with an international team to develop next-generation spectroscopy instruments based on bright, coherent, broadband infrared sources and field-resolved detection. The goal for this position is to implement novel nonlinear techniques to boost the detection limit to the next level. The duration of the position is initially 2 years, with the possibility of extension. The place of work will be at the LAP in Garching, near Munich (Germany).

The position is supported by the Ferenc Krausz Fellowship, one of the Institute’s Nobel Laureate Fellowships awarded to outstanding postdocs. These offer early career scientists a unique insight into the research of the Nobel laureates at the Max Planck Institutes. In addition, the selected postdocs will also profit form the outstanding national and international networks for furthering their careers.

Qualifications and Skills

• Excellent PhD in physics or a related discipline
• Experience with ultrafast lasers and nonlinear optics
• Experience with infrared and/or nonlinear spectroscopy is a plus
• Strong self-motivation and the ability to solve problems independently
• Professional English proficiency
• Stimulating projects with many learning opportunities
• Supportive, multi-disciplinary and highly motivated team
• Access to the latest optical technologies and state-of-the-art laboratories
• Excellent research and working conditions for building your research career

Your application

Please send a brief cover letter explaining your interest in the position, your CV and contact information of two references.

For questions and applications, please contact Dr. Kafai Mak: [email protected]

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April 22, 2024

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An advanced brain science tool that doesn't require coding expertise

by Verena Schulz, Helmholtz Association of German Research Centres

Researchers at Helmholtz Munich and the LMU University Hospital Munich introduce DELiVR, offering a new AI-based approach to the complex task of brain cell mapping. The findings are published in the journal Nature Methods .

The deep learning tool democratizes advanced neuroscience by eliminating the need for coding expertise. DELiVR empowers biologists to investigate disease-related spatial cell dynamics efficiently, fostering the development of precision therapies for enhanced patient care.

Democratizing 3D brain analysis

Many diseases are linked with changes in the expression of certain proteins in the brain. To study those changes, scientists examine how they change during disease progression in model organisms. Imaging entire mouse brains generates vast data sets, necessitating accurate quantification methods for its meaningful interpretation. However, identifying labeled cells within large 3D image data is challenging.

While artificial intelligence (AI) holds promise for data analysis, it typically requires extensive data annotation and advanced coding skills, restricting its use to specialized labs. The research team therefore aimed to overcome these barriers, democratizing 3D analysis for broader scientific access.

Virtual reality empowering researchers

To accurately quantify specific cells within brain images, the research team initially trained an AI algorithm to identify them in 3D microscopic images. Leveraging virtual reality (VR) for label generation, the researchers immersed themselves in the images, annotating cells directly in 3D—a faster and more precise method than traditional 2D slice-based approaches.

Subsequently, the team employed these VR-generated labels to train an AI algorithm for the automatic identification of active neurons. They integrated the processes of detecting cells, matching them to a brain atlas , and visualizing the results into their DELiVR (Deep Learning and Virtual Reality mesoscale annotation) pipeline.

The system operates seamlessly with Fiji, an open-source software for image analysis, in an end-to-end workflow. DELiVR also features a customizable function allowing researchers to train it for specific cell types, such as microglia, an essential immune cell in the brain, demonstrating its adaptability for diverse research projects.

"In essence, DELiVR offers a seamless solution for identifying and analyzing cells throughout the entire brain, providing invaluable insights into their roles and behaviors in both health and disease—all without requiring coding expertise from scientists. DELiVR represents a step towards developing new therapeutic interventions that could ultimately improve the quality of life for individuals affected by debilitating conditions," says Prof. Ali Ertürk, who led the development of the tool at Helmholtz Munich.

Use case: Cancer-related weight loss

To demonstrate the power of DELiVR, the research team exemplified its capacity to transform our comprehension of how cancer influences our brain activity. Concentrating on the significant clinical challenge of tumor-induced weight loss, they discovered specific brain activity patterns distinguishing cancers that induce weight loss in mice from those that do not.

Dr. Doris Kaltenecker, a first author of the study introducing DELiVR, says, "Our findings using DELiVR have revealed potential therapeutic targets within brain regions. This might pave the way for promising strategies to combat cancer-related weight loss in the future."

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Trellis.Law Blog

We make the law accessible so everyone can make the right decisions, maximizing your legal edge this summer: law student glow-up with state trial court and judge analytics.

Summer is approaching, and for those in law school, this means internships, clerkships, pivotal research projects for law review or moot court, and bar review.  Indeed, at every turn, law students face the challenge of ensuring their work-product rises to the top in a competitive legal landscape. So today, we look at use cases for Trellis to help you prepare for your best summer yet as a budding legal professional.

One of the primary benefits of Trellis’ legal analytics platform is the depth and breadth of data available. Users can access detailed profiles of judges that include their ruling histories, motion granting rates, and even the average length of their cases. This information is crucial for those preparing for internships, clerkships, or actively engaged in case preparation, as it provides a strategic overview of what to expect in court.

With vast amounts of data related to cases, judges’ rulings, tendencies, and timelines, you will have unprecedented insights into courtroom behaviors and decisions. Thus, utilizing the platform can transform your legal research, preparation, and career development.

Tailored Case Strategy Development

Interning law students and clerks often need more real-world experience to prepare legal strategies and briefs. Analytics platforms can alleviate this pain point by offering insights derived from historical data, such as how a judge typically handles particular types of motions or legal issues. Imagine crafting more informed, precise, and potentially successful legal arguments. A bonus with Trellis is the plethora of actual litigation documents and tentative rulings . This means that you have more than judicial analytics at your fingertips. You have a fantastic brief bank to assist you in your motion-drafting journey.

Judge analytics platforms are powerful academic resources for law review and moot court competition candidates. They allow students to conduct empirical research or analyze trends across various jurisdictions and legal topics, enhancing law note proposals and competition briefs and providing a richer educational experience. Additionally, these tools will help you understand the practical applications of legal theories you’ve learned in class, bridging the gap between theoretical knowledge and practical, real-world application.

Career Development and Networking

Understanding a judge’s legal philosophy and decision-making process can be invaluable for law clerks and interns aiming to advance their careers. Legal professionals can stand out in competitive environments by demonstrating awareness and adaptability to a judge’s preferences and tendencies. Moreover, this knowledge facilitates better networking with peers and professionals who share similar judicial interactions, fostering connections that could lead to career opportunities.

Showing Up Well Equipped to Win

For law students, clerks, law review, or moot court candidates, judge analytics platforms are more than just a research tool; they are a bridge to deeper understanding, better preparation, and more robust professional development. By leveraging the detailed insights these platforms provide, you can navigate the complexities of the legal world with greater confidence and precision.

Today, investing time in learning and utilizing legal analytics platforms is essential for those in the legal field. Analyzing and predicting judicial behavior offers a competitive edge in academia, internships, and beyond. Embrace these tools, and you are not just preparing for a case; you are preparing for a successful career in law.

Looking to Level-up for Law School and Beyond?

Check out  Trellis ! Trellis is an AI-driven, state trial court research and analytics platform. We make the fragmented U.S. state trial court system searchable through a single interface, offering comprehensive insights into judges, cases, and opposing counsel. Effortlessly track lawsuits across states and stay updated with ongoing litigation documents.  Request a demo  today and elevate your legal practice with our intuitive analytics and API.

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