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EPFL, the Swiss Federal Institute of Technology in Lausanne, is one of the most dynamic university campuses in Europe and ranks among the top 20 universities worldwide. The EPFL employs more than
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interpret dynamic visual signals. The successful candidate will contribute to experimental design, computational analysis of behavioral data, and the integration of biological insights with robotic systems
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are looking for a highly motivated post-doctoral candidate interested in advanced health data interoperability and harmonisation, federated data analysis and the development of next generation AI-based research
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regions by developing interpretable and efficient methods in comparative pangenomics, leveraging machine learning methods, statistical analysis and efficient algorithm and data structures (https
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, Skills, and Abilities Skills in computational fluid dynamics, geometric morphometrics or 3D shape analysis, and kinematics. Preferred Qualifications Strong backgrounds in vertebrate and functional
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modeling and environmental data analysis, such as Python, MATLAB, Fortran, C/C++, or comparable languages and tools. Ability to work with large datasets and to integrate these data with acoustics models
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offers the opportunity to work in a dynamic, multidisciplinary environment with access to state-of-the-art microbiological and molecular biology tools. Key Responsibilities Design and conduct research
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peer reviewed journals 60% Potential dependent ab initio molecular dynamics simulations 20% data analysis and dissemination 10% preparation of project reports for sponsors and meeting with sponsors and
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new, creative, and dynamic research team. The focus of Dr. He’s lab is to study the roles of T lymphocyte during autoimmunity and infection. The job responsibilities include: To independently undertake
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microscopy, image analysis, and programming for the automation of image quantification - In-depth knowledge of membrane biology, particularly regarding the structure, content, and dynamics of the endoplasmic