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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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Master's degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations Research or a related field Strong knowledge of quantitative and/or computational research methods, ideally in
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Systems, Economics, Mathematics, Operations Research, Computer Science, or a related field Strong knowledge of quantitative and/or computational research methods, ideally in optimization and simulation
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contribute to shape the energy transition in Luxembourg. For further information, please contact Your profile Master's degree in Energy Policy, Energy Economics, Information Systems, Mathematics, Operations
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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: · Developing mathematical optimization approach and tool for optimal operation of smart low voltage grids with high penetration of distributed energy resources · Producing quality research outputs (e.g
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biological physicists who ask how physical mechanisms shape functional biological patterns. We combine statistical physics, nonlinear dynamics, mathematical modeling and data-driven simulation with physics
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The EpiSignal RTG (www.grk3112.uni-stuttgart.de/ ) funded by the Deutsche Forschungsgemeinschaft (DFG) studies the crosstalk of intracellular signaling pathways and chromatin modification networks. EpiSignal aims to increase the molecular understanding of how the complex networks of cell and...
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(FSTM) at the University of Luxembourg contributes multidisciplinary expertise in the fields of Mathematics, Physics, Engineering, Computer Science, Life Sciences and Medicine. Through its dual mission
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individual with a MSc degree in computer science, mathematics, chemistry, computational biology or a related subject. The ideal candidate has familiarity with one or more of the following areas: algorithmics