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, machine learning, energy technology or related subjects Prior experience in building predictive models using regression techniques, neural networks (CNN, GNN) or symbolic regression Experience in
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) Intense interaction with consortium Your Profile: Master and PhD degree in materials science, physics, chemistry, informatics, machine learning, energy technology or related subjects Prior experience in
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11.12.2025 Application deadline: 15.02.2026 The Faculty of Science at Tübingen University invites applications for a W3-Professorship in Machine Learning in Physics at the Department of Physics (m/f
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of Mathematics and Natural Sciences at Heinrich Heine University Düsseldorf is inviting applications for the position of a Professorship for Machine Learning (open rank: W2 or W1 with tenure track to W2
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and manufacturing technologies for the manufacture of innovative products. We develop new machines and processing strategies on behalf of customers, optimize existing production systems and implement
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Max Planck Computing and Data Facility (MPCDF), Garching | Garching an der Alz, Bayern | Germany | 3 months ago
for a Data Scientist or Machine Learning / AI expert (m/f/d) Kennziffer 07/2025 to join the AI and High-Performance Data Analytics division of MPCDF. In this role, you will contribute to a variety of
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12.08.2025, Professuren The Technical University of Munich (TUM) invites applications for the position of Professor in »Machine Learning for Sustainable Processes and Materials« W3 Associate
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Development effective at the earliest possible date: W1 Professorship in Machine Learning in Agricultural and Food Economics (f/m/d) The professorship will initially be awarded with civil servant status for a
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Field of study: computer science, mathematics, software design, software engineering, technical computer science or comparable. Machine Learning (ML) models are reaching a maturity level that allows
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, non-linear contact interactions, and varying surface conditions. Reinforcement learning (RL) offers a promising approach to develop adaptive and robust control policies, but training on physical