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design of complex physical systems. The group is jointly embedded at Saarland University and DFKI in Saarbrücken. Current Research Directions include: AI for scientific computing – neural operators and
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sustainable operation of future energy networks by combining our knowledge of energy systems with cutting-edge developments in machine learning, generative AI, and digital infrastructures. Your Job
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Learning at TUM works on machine learning, artificial intelligence, and information processing, with a current focus on foundation models, data-centric research, and applications to scientific and medical
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diversity methods, data management, reproducible code, R/Tidyverse, machine learning and AI for ecologists, visualisation, evidence-based policymaking, science-policy communication, and policy brief writing
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problem. Do a theoretical analysis of the assigned problem using tools from information theory and related fields. Design a solution which is implementable on a computer and in hardware. Collaborate with
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fields. Design a solution which is implementable on a computer and in hardware. Collaborate with colleagues to implement the solution in hardware at the ACES lab at the Institute of Theoretical
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CFD simulations, led to a shift from performance towards efficiency, defined more precisely as minimisation of computer resources required for a given simulation. Concomitantly, the objective
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contribute to current challenges in biology and medicine Candidates with a strong ambition to contribute to and shape excellent scientific research We offer international working environment with communication
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student works on a current research topic in the area of computer science in one of the computer science research groups of the University of Göttingen. The research group should be chosen based
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profile is shaped in particular by five high-profile areas. About Minds, Media, Machines Minds, Media, Machines (MMM) is one of the five interdisciplinary, high-profile areas that largely define