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, optimization, control, probability/statistics, game theory, mechanism design, or machine learning (at least one) Programming experience (e.g., Python, Julia) Strong analytical thinking and problem-solving
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on reinforcement learning (RL) for policy discovery in a multi-sector “integrated modeling environment” that connects fast ML metamodels of simulators (e.g., transport, energy, environment, climate events). The aim
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for candidates with strong analytical skills, curiosity, and enthusiasm for cryptographic research. A solid background in theoretical computer science, mathematics, or a related field will be considered an asset
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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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, certification, and long-term performance of next-generation wind turbine components enhanced with meta-materials. We are seeking a motivated, analytical, and ambitious researcher who is passionate about enabling