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develop a physics-informed digital-twinning approach to better characterize these dynamics and estimate relevant electrical and control parameters from transient voltage and current data. Your tasks will
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design of hybrid electrical–optical computing architectures and (2) develop learning rules that are both technology-feasible and well-suited for machine-learning workloads. The project will consist among
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, and physics-informed machine learning open new possibilities for establishing grid foundation models. However, it is still unclear which model structures is most capable and suitable, how well they
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consumption on complex algorithmic or cognitive tasks. This project is part of the ELEVATE MSCA Doctoral Network (https://www.elevate-dn.eu/) and co-supervised by our partners at the university of
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applications and algorithms that can leverage associative memories, including machine learning and LLMs, genomics, and classical data science methods (tree models, decision trees, finite automata). We
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processing conditions Apply data-driven and machine-learning methods for multi-objective optimization of efficiency, reproducibility, and operational stability Study intrinsic material stability
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Your Job In this position, you will join our Simulation and Data Lab for AI and Machine Learning for Remote Sensing . The lab advances interdisciplinary research and operational services by combining