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the real world based on a seamless combination of data, mathematical models, and algorithms. Our research integrates expertise from machine learning, optimization, control theory, and applied mathematics
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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 algorithms
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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 algorithms
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control and reinforcement learning supported by an edge-cloud-based wireless communication environment. The doctoral student will work on data-driven theory and method development in simulation environments
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system modelling. Solid knowledge in mathematics, physics, thermodynamics, energy technology, optimization, and programming for system modelling, with experience in tools such as Matlab, or Python
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thermodynamics, energy technology, or systems modelling. Solid knowledge in mathematics, physics, thermodynamics, energy technology, optimization, and programming for system modelling, with experience in tools