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Field
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into the same devices. The research project is part of a larger consortium, gathering world-class researchers in remote sensing with expertise ranging from estimation and optimization theory to hardware design
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to optimize their durability and efficiency under industrial conditions. The goal is to contribute to a sustainable energy storage solution while avoiding the use of expensive metals. The postdoctoral
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to the large-scale nature, complexity, and heterogeneity of 6G networks, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal
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optimization and Al tools to coordinate and optimize the role of ESS within the onboard microgrid. This project finds it context in the urgent need to reduce greenhouse gas émissions and improve the energy
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of dynamical systems, which will be integrated into large-scale optimization frameworks to enhance the efficiency and reliability of power grid operations. The Postdoctoral Appointee will be responsible
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. The group will be contributing to the Physics Modeling (MC software, MC validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous
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primarily research on Reinforcement Learning, and/or Optimal Control, and/or Model Predictive Control. RISC invites qualified applicants in the areas of electrical, computer, or mechanical engineering, or
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will include: Establishing and optimizing cell culture protocols under sterile conditions (adherent or suspension eukaryotic cell lines). Designing, establishing protocols and conducting recombinant
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, etc.). Leveraging computational geometry methods for advanced manufacturing applications. Advancing topology optimization methods for lightweight and high-performance designs. Collaborating with faculty
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structures and evaluate their performance in optimized MIM (Metal-Insulator-Metal) stacks, in order to identify their potential for industrial application. This project, financed in the framework