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component, particularly magnetic components, Optimization and surrogate-modeling in Python, Integration of machine learning and numerical methods. We encourage applications from candidates with a strong
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-efficient, open-loop optimisation of fermentation control profiles, building on recent theoretical developments in optimal control theory, reinforcement learning and numerical methods as well as laboratory
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design and integrity management of the support structure of the subsea HVDC electrode unit, supported by experimental tests and validated numerical models, to enable lightweight, reliable, cost-efficient
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complex materials simulations. These agents will assist with setting up, executing, and optimizing electronic structure workflows, from standard ground-state Density Functional Theory (DFT) calculations