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-level insights. Your work will be essential to exploring large design spaces, predicting device behavior, and identify optimal parameters without the prohibitive cost of fabricating numerous physical
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aero-elasticity, numerical flow computation and experimental work. You will work with nearby colleagues and international collaborators and be an active contributor to the ongoing project formation and
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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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at the frontline of the rapid developing field of spatial omics. Opportunities for professional development and career advancement. A collaborative work environment with numerous partners. Access to state-of-the-art
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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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approximation algorithm, using linear programming methods. Over the next years, the project will grow to a collaborate team of 4-5 PhD students and Postdocs. The successful candidate will work directly with the
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in education, health, and policy evaluation. The postdoc will join the Econometric Theory and Methods Unit (ETMU) at UCPH (https://www.economics.ku.dk/research/ResearchCentres/econometric-theory-and