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Field
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mechanisms, Sensors and instrumentation, Data analytics, machine learning, or AI, Digital twins or physics-based modeling, wide-bandgap semiconductor technologies. Excellent written and spoken English
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, Information & Computer Engineering, Digital Engineering, or equivalent We Offer Interesting area of responsibility Collegial and friendly working atmosphere Flexible working schedule (e.g. possibility for home
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looking for a PhD candidate to work on research at the intersection of machine learning, data privacy, and medical imaging. The position is part of a three-year research project that investigates how
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simulation results with experimental data. This project will integrate advanced AI techniques, including machine learning for parameter optimisation (e.g., Bayesian optimisation, reinforcement learning), AI
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, engineering physics, physics, machine learning, or in a similar subject, or have completed at least 240 credits in higher education, with at least 60 credits at Master’s level including an independent project
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generative AI framework that utilizes machine learning predictions and quantum chemistry simulations to design stable, synthesizable, high-performance molecules. The framework will integrate multi-objective
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EU programme Is the Job related to staff position within a Research Infrastructure? No Offer Description PhD Position in Cryogenic Carbonate Mineralization and Carbon Dioxide Fluxes The Geomitigation
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 1 hour ago
) in Geometric Deep Learning to join the Machine Learning and Artificial Intelligence (MLI) g roup to perform research in geometric deep learning for materials science. This research is part of
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. Project Overview The project focuses on developing and applying advanced CFD models for aeroengine oil systems. There will also be opportunities to integrate machine learning techniques for building lower
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machine learning and conventional optimisation techniques. 2. To design and optimise magnonic primitives for wave-based neuromorphic computing, including programmable devices enabling nonlinear activation