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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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live in. Your role The PhD candidate will generate and study complex macroscopic flow patterns of LCE precursors, polymerize the precursors into LCEs programmed by the flow patterns, and study the
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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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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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degree or equivalent in Robotics / Aerospace / Mechatronics / Mechanical or Electrical Engineering, or related fields in Engineering or Computer Engineering Programming Skills: Design tools for Mechanical
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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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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