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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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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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optimization of optical imaging hardware, develop data acquisition software and algorithms for data processing, as well as perform phantom and human clinical studies. This candidate is expected to co-supervise
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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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future data management / governance approaches. Aim is to optimize the shared service model enabled by improved data sharing, deployment of digital technologies and redesigned workflows. With this project
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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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demonstrating a proof of concept for low-power logic gates based on spin waves. The work involves experimental research. The post-doc will be responsible for: - the growth and optimization of magnetic thin films
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and optimize TD-RAS (time-dependent restricted-active-space) methods for many-body systems including positrons. - Carry out large-scale numerical simulations of photoionization and de-excitation
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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