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We are looking for a motivated summer intern to join a collaborative project aimed at improving the performance of superconducting qubits by optimizing noise filtering in dilution‑refrigerator
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mathematics to work with Axel Ringh on a project funded by the Swedish Research Council (VR). The project is centered around inverse optimal control/inverse reinforcement learning, both for continuous-time and
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situated in the field of machine learning. Potential research topics include, but are not limited to, algorithmic knowledge discovery, graph mining and social network analysis, optimization for machine
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application! We are looking for a PhD student in automatic control. Your work assignments You will work within a project focused on optimization, planning, and decision-making in control systems involving
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computational tools, including optimal-control techniques, that directly support ongoing experiments and advance the control of bosonic quantum operations. About us The position is hosted at the Applied Quantum
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Council (VR). The project is centered around inverse optimal control/inverse reinforcement learning, both for continuous-time and discrete-time systems. In particular, we are looking for a strong candidate
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potentially involving techno-economic analysis and AI-driven models for optimizing design and operation. Activities within project management and co-supervision of graduate students are also foreseen
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charging stations is influenced by different system solutions. Methods to be used include data analysis, modeling, simulation, and optimization. The research is part of a larger project that also, in
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of Applied Mathematics and Statistics at the Department of Mathematical Sciences. Under the supervision of Malin Rau, you will contribute to impactful research in optimization. About us The department of
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examines how digital twins, simulation-based multi-objective optimization, and AI-driven decision support systems can enable manufacturing industries to maintain stable production, optimize energy usage, and