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qualified Postdoctoral Research Associate. The incumbent will join a collaborative team to combine synthetic magnetosome biogenesis and computationally optimized cobalt-binding peptides to enable efficient
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validation and Pileup modeling), the MET High-Level Trigger validation, optimization and performance studies, and to the heterogeneous computing where the focus will be to work on to the current efforts
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machine learning models (e.g., VAEs, GANs, transformer-based models) to large-scale biomedical and biological data, including developing and optimizing models to predict disease progression and create
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thinking with renewable energy community planning, developing methodological innovations that bridge quantitative modeling and qualitative approaches. Your tasks Develop and apply optimization and simulation
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obtained by MOCVD deposition. A PhD student at LMGP is currently developing and optimizing the operating conditions to control nanowire geometry, network density and connectivity, and coverage. The recruited
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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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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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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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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