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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and
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and optimization, we use tools such as artificial intelligence/machine learning, quantum conputing, graph theory, graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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and Histology, which we use for models training and validation. Objectives A key research focus of our group is the optimization of medical image-to-image translation models. In the present project, we
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Testing and Experimentation Facility (TEF) for the energy field. Specifically, it leverages AI and cutting-edge infrastructure to optimize EV charging and energy systems. By integrating distributed energy
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processing, and convex/non-convex optimization. Furthermore, our activities are experimentally driven and supported by the COMMLab, the 6GSPACE Lab, the HybridNetLab, the QCILab, TelecomAI Lab, CSAT Lab, our