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for the diagnosis and prediction of lithium-ion battery ageing. About us At the department of Electrical Engineering research and education are performed in the areas of Systems and Control, Communications, Signal
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robust optimization integrated with advanced adversary models and control frameworks such as model-predictive control or reinforcement learning. The exact details of the project will be decided in a
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robotics – reinforcement learning, whole-body model predictive control (MPC), and differentiable optimal control – to simulate human balance and step recovery in urban transport scenarios. The goal is a
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carried out and enrich the knowledge base (i.e. learning by interaction); (iii) querying the knowledge base about what was useful in the past to predict actions that might be useful in the present, try them
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enrich the knowledge base (i.e. learning by interaction); (iii) querying the knowledge base about what was useful in the past to predict actions that might be useful in the present, try them out and update
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terrestrial ecosystems to better understand how soil microorganisms regulate global biogeochemistry which can be used to predict responses to environmental changes. We are a group of microbial ecologists
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and optimization, including model predictive control and reinforcement learning. The aim is digital-twin-based decision support for electrified groundwork construction. The position is placed
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machine learning for neuromorphic computing for predictive materials discovery. The doctoral student will be part of a larger project Brain-inspired AI Design of Topological Magnets for Sustainable
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the department of Electrical Engineering research and education are performed in the areas of Communications, Antennas and Optical Networks, Systems and Control, Signal processing and Biomedical
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. At the Division of Systems and Control , we develop both theory and concrete tools to design systems that learn, reason, and act in the real world based on a seamless combination of data, mathematical models, and