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- NTNU - Norwegian University of Science and Technology
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physics. By combining experimentally grounded models of realistic quantum systems with abstract theoretical frameworks from quantum information and the foundations of quantum physics, you will establish
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-Funded PhD Positions in Theoretical Chemistry and Physics, Department of Quantum Nanoscience, TU Delft. Job description Three PhD positions are available The Alessio group at the Department of Quantum
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candidate holding a Master’s degree (or equivalent) in Physics with an excellent academic record. The ideal applicant combines strong analytical ability with a genuine interest in theoretical physics and
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embedded into cyber‑physical systems (CPS) such as autonomous vehicles, smart grids, industrial control systems, robotics, healthcare devices, and intelligent transport infrastructure. While significant
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Are you fascinated by controlling magnetic matter by femtosecond laser pulses, eager to explore the underlying physical mechanisms, and passionate to develop a generic tool to ‘print’ complex
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theoretical computer science, and to combine those in new innovative ways. The position will offer you the opportunity to work on important problems with real-world impact and a strong publication potential. As
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building physics, especially focusing on the Arctic conditions. Project description This research explores how mining waste, such as tailings and waste rock, can be reused rather than sent to landfill
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knowledge for a better world. You will find more information about working at NTNU and the application process here. About the position As agile development scales to larger, more complex organizations
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safety, ecosystem degradation, and declining water quality. Given the growing challenges faced by these environments, there is an urgent need to improve our understanding of the physical, ecological, and
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reinforcement learning, learning-based adaptation and control, physics-informed machine learning, learning-enhanced model predictive control, control-theoretic safety and stability guarantees for learning-enabled