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and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among others of the following tasks: Investigate and design
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency
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Max Planck Institute for Biological Cybernetics, Tübingen | Bingen am Rhein, Rheinland Pfalz | Germany | about 1 month ago
interacts with the prefrontal cortex to integrate interoceptive signals and guide higher-order cognition. Combining ultra-high-field layer fMRI at 9.4 Tesla, diffusion MRI, and machine learning in humans with
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Helmholtz Zentrum München - Deutsches Forschungszentrum für Gesundheit und Umwelt | Stein bei N rnberg, Bayern | Germany | about 1 month ago
, Physics, Engineering, Computer Science or related disciplines with a strong academic record. Strong background in data analysis, statistics, machine learning, scientific computing, or related computational
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learning-based surrogates for physical systems LLMs and scientific agents – large language models that autonomously reason, plan and execute scientific workflows AI for engineering design – LLM-driven agents
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Campus (LLEC). Development of physics-informed and graph-based machine learning methods for energy system monitoring, forecasting, and planning Data analysis considering uncertainties, missing data
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data • Design clinically meaningful benchmarks and robust evaluations • Publish at leading machine learning and medical AI venues • Collaborate with clinicians, computer scientists, and European partners
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evaluation strategies. In close collaboration with chemists, engineers and data scientists, a platform is being developed that combines materials development, process optimisation and machine learning
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Heidelberg Institute for Theoretical Studies (HITS gGmbH) | Heidelberg, Baden W rttemberg | Germany | about 13 hours 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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the Department of Mechanical Engineering at the School of Engineering and Design (SoED) of the Technical University of Munich (TUM) in Garching, focusing on the design and evaluation of human-machine interactions