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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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*• 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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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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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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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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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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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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and growing scientific team, with opportunities to shape the direction of an exciting research program at the intersection of gene regulation, stem cell biology, and machine learning. This is an
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equivalencies: https://hr.uky.edu/employment/working-uk/equivalencies Required Related Experience No experience required. Required License/Registration/Certification Registered Nurse (RN) and must advance on UKHC
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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