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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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determination, and extraction of optical properties. A central part of the work will be to investigate how LLMs can assist in setting up calculations, choosing sensible numerical parameters, and monitoring
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begin with designing appropriate device geometry employing a broad spectrum of analytical, semi-analytical, and numerical techniques. Based on the results of the design process, a device will be
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project’s principal investigator, Associate Professor Lars Rohwedder, an internationally recognized expert in the areas of approximation algorithms and parameterized algorithms, see https://larsrohwedder.com