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will join an interdisciplinary team working across electrolysers, Power-to-X systems, power electronics, control, digital twins, and energy-system optimization, in close collaboration with the Technical
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for Applied Power Electronic Systems. You will join an interdisciplinary team working across electrolysers, Power-to-X systems, power electronics, control, digital twins, and energy-system optimization, in
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scientific AI, system modelling and identification, as well as optimization and control. Your work will focus on developing new methods that integrate first-principles models with data-driven learning
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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perception, optimization, or control will be an advantage. The candidate should be comfortable with scientific programming, for example in Python and common machine-learning frameworks such as PyTorch
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tackle the socio-techno-economical complexity of the built environment. Your vision is to close the “performance gap” by integrating Advanced HVAC System Design & Control, Cyber-Physical Modeling, and
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such as process modelling, thermodynamics, heat and mass transfer, process dynamics and control, optimization or energy-system analysis, and you are interested in applying these disciplines to Power-to-X
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optimization, techno-economic assessment and model validation. The research will also examine the coordinated operation and control of Power-to-X processes, electrolysers and energy-storage systems in
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Technology, and the PhD student will be positioned in the Esbjerg Energy section. The position is part of the internally funded research project SURGE: Speed-optimized USVs for Robust Guidance and Offshore
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while maintaining computational efficiency through lower-fidelity simulation of non-critical regions, (ii) virtual sensing techniques for load and stress estimation from limited and optimally placed