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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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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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researchers focussing on modelling, estimation and prediction related to battery systems, ranging from details on micro-scale in cells to cloud calculations for fleets of electric vehicles. About the research
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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