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diffraction data where the information extends towards 3-d space. Machine learning offers promising approaches for the solution of complex problems of disorder, ultimately aiming at general and automated
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Description The overarching mission is to conduct research combining machine learning, data assimilation, and physical modeling to enhance short-term (days/weeks) forecasts of Arctic sea ice conditions. The
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innovative machine learning architectures for the mining, prediction, and design of enzymes. Combine state-of-the-art ML (e.g., deep learning, generative models) with computational biochemistry tools
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of machine learning technologies, including large language models within the Department of Food and BioResource Technology, with a special focus on technologies applicable in so-called developing countries. As
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*• Experience in Python or another programming language (projects, GitHub repositories, courses, scientific use).• Training or experience in machine learning and data science applied to environmental or energy
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or equivalent Skills/Qualifications The work and responsibilities of the researcher will include the following research topics: Knowledge in: Computational neuroscience Machine learning / AI Biomedical data
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project “Analytics for Learning with Machines” (ALMA) The position is TV-L E13, 75%, limited to 3 years, funded by the Deutsche Forschungsgemeinschaft (DFG). The project is a Franco-German collaboration
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capable of understanding, learning, and acting in complex, dynamic settings. The lab’s work lies at the intersection of computer vision, multimodal learning, and robotics, advancing next-generation embodied
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
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the intention of the subject. The advancement in brain decoding models benefits the development of brain-machine interfaces, Neuroprosthetics and the understanding of neurological disorders such as