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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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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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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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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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NAS that considers accuracy, latency, energy, and carbon at the same time. Delivering a carbon-aware NAS framework that generates Pareto-optimal model architectures and model libraries whose variants
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design workflows. • Analysis of antibody-antigen interactions to guide antigen optimization and vaccine development. The exact project will be tailored to the interests and background of the successful