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partnership with the Odysseus Space company as part of the Simulator development for the optical slant path program. This PhD project aims to develop an analysis tool for the optical ground-to-satellite links
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students who are eager to develop and apply artificial intelligence techniques and mechanistic mathematical models to explore fundamental questions in biology. The PhD program is organized in partnership
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management Guide M.Sc. students Participate in a dedicated training plan For further information, please contact Prof. Gabriele LENZINI at info- and Prof. Pedro CARDOSO-LEITE Your profile The ideal candidate
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to accelerate the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus
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Energy Transition NCER Project (D2ET). This project aims to accelerate the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support
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the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support decision-making for an optimal energy system, with specific focus on cost
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Energy Transition NCER Project (D2ET). This project aims to accelerate the energy transition in Luxembourg by co-creating an ambitious research program. It will utilize a data-driven approach to support
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/electrical engineering, or a related field.Strong interest in automation and laboratory robotics.Basic experience in wet-lab microbiology techniques.Willingness to learn programming and computational analysis
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in a dedicated training plan For further information, please contact Prof. Marcus Völp at critix- Your profile Qualification: Master in Computer Science and/or Cybersecurity or equivalent degrees with
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Knowledge of electronics, programmable logic, and processing systems is an advantage Strong analytical and programming skills are required (Python, VHDL, Matlab, and C/C++). Prior proven experience in data