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-timescale optimization, and integrated Power-to-X systems. You will drive the development of system-level digital-twin and optimization methodologies, integrating electrolyzer models with renewable generation
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works with modeling, simulation, control, optimization, and experimental validation of fluid and energy systems, with applications including Power-to-X, carbon capture and utilization, green fuels, and
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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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, deterministic systems. You model them, you optimize them, you operate them. That paradigm is breaking down. As electricity replaces fossil fuels, industrial systems must operate under fundamentally new conditions
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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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relation to the research group Fluid Power and the PhD Student will be positioned to the section for Mechatronics. Your competencies The ideal candidate for this PhD position should have a strong academic
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the system to learn optimal adjustment policies directly from interaction with the physical infrastructure. Anomaly detection and fault diagnosis. The candidate will develop a multi-level diagnostic framework