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leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy resources, demand response, and storage, it aims to enhance grid flexibility and
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involve various aspects of the modeling and the optimal control of active systems. The candidate is also expected to take part in the scientific activities organized within Physics of Active Matter group
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Luxembourg Institute of Science and Technology (LIST) | Luxembourg, | Luxembourg | about 2 months ago
digestate, advancing the technology from bench to pilot scale (TRL 4–5). The work encompasses three main areas: (1) process optimisation, using AI-assisted experimental design to identify optimal operating
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, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab, the 6GSPACE Lab, the HybridNetLab, the QCILab