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enabling process monitoring, learning, and policy training while ensuring safety and efficiency. For this, we develop novel learning-based control methods with practical applicability and theoretical
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Automatic Control group (from Department of Electrical Engineering) and the Mathematical Optimization group (from Department of Mathematical Sciences). About the research project The project focuses on
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optimization English, French and German as a plus Our offer Empa offers a dynamic and inspiring research environment at its Thun campus, located near Bern. You will work on a scientifically and
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, especially the areas of partial differential equations, integral equations, fast algorithms, spectral and high-order methods * machine learning, especially the areas of optimization, learning theory
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-scale infrastructure systems. For more details, see https://research.chalmers.se/en/project/12873 Who we are looking for The following requirements are mandatory: A doctoral degree in applied
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to the applicant him/herself. You can read about the recruitment process at https://employment.ku.dk/faculty/recruitment-process/ The applicant will be assessed according to the Ministerial Order
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each applicant has the opportunity to comment on the part of the assessment that relates to the applicant him/herself. You find information about the recruitment process at: https://employment.ku.dk
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studies and provide the substrate necessary for artificial intelligence (AI) guided experimental design and optimization of scarce/costly resources, such as animal models. As AI capabilities advance
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity statistical
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sequential experimentation Bayesian optimization and active learning contextual bandits, reinforcement learning and online experimentation causal inference and treatment-effect heterogeneity