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include Master’s-level in Mechanical Engineering, Aerospace Engineering, Control Engineering, Wind Energy, or a closely related field. Strong background in control theory, fluid dynamics, structural
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(density functional theory and ab-initio molecular dynamics simulations) with artificial intelligence techniques to parameterize machine learning force fields and kinetic Monte Carlo methods to model
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strain theory and advanced hyper elastic material models incl. anisotropy You preferably have insights into topology optimization and/or finite element analysis and are committed to improving state
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strong multi-disciplinary focus on energy markets, optimisation, game theory, control and machine learning. The EMA section (https://wind.dtu.dk/research/research-divisions/power-and-energy-systems
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theory around the pace-of-life of marine fish and simulate fast and slow life history strategies within fish populations and fish community food webs. This research aims to examine how these strategies
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on the most advanced theories, models, using first-hand cases and data from the wind energy industry, to lift our understanding of wind and wave characteristics during storms in the presence of wind farms
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techniques to manipulate material functionalities using tailored laser fields and optical cavities. By joining this network, you will contribute to advancements in: Controlling light-matter interaction and the
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competencies in the intersection between theory and real-world battery application by operating, monitoring, analyzing, modeling, and controlling the modern BESS. As part of the N5T PhD program, your tasks will
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members working on novel high-resolution microscopy techniques to experimentally quantify transition probabilities, alongside density functional theory (DFT) calculations of defects in feldspar. This inter