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backbone of tomorrow's Power-to-X plants. This 3-year PhD position offers a unique opportunity to build a research career at the intersection of digital twins, multi-timescale optimization, and integrated
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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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applications for a three-year PhD Stipend from October 1st or soon hereafter in dynamic modelling, simulation and optimization of Power-to-X processes for renewable fuel production. The project is primarily
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Aalborg University, Department of Energy, Campus Esbjerg, invites applications for a three-year PhD Stipend from October 1st or soon hereafter in dynamic modelling, simulation and optimization
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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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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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research has focused on optimizing materials chemistry to improve performance. Despite these advances, the fundamental processes governing electrochemical interfaces, particularly their degradation and
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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
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part of the interdisciplinary Novo Nordisk Fonden national project housed at CED, you will work in collaboration with other PhD students and a postdoc, and will have ample opportunities to interact with