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Some of the biggest open questions in modern physics concern the limits of the Standard Model and General Relativity. The nature of dark matter and dark energy remains unknown, and many theories
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physics-informed neural networks (PINNs). However, these approaches are still in their early stages of development and have yet to demonstrate their effectiveness for complex real engineering problems
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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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Join us! Are you a curiosity-driven scientist with a background in physics, engineering, or a related discipline, who wants to lead work at the intersection of robotics and metamaterials? Join us
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investment yet in the vibrant and strategically important field of Metamaterials research. Explore a career and grow your experience of commercial research and professional life in and around the Physical
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PhD Studentship: Efficient Long-Horizon Task Execution in Physical AI (deep learning, computer vision, robotics) Number of awards: 1 Award information: Fully funded PhD studentship covering Home
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. Delivered in collaboration with Intel, including industrial co-supervision. The student will work with Durham University academic supervisors and an Intel co-supervisor on compact Physical AI, data-efficient
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Polymer manufacturing is highly energy intensive, and achieving net zero requires more than fuel switching. This project focuses on process systems engineering for industrial decarbonisation
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unique opportunity to gain expertise that is highly sought after across pharmaceutical and advanced manufacturing industries. This project is part of the EPSRC CDT in Process Industries: Net Zero . The
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Phd Studentship in Computer Science: Empirical Security Assessment of AI Decision Engines in Cyber-Physical Systems Award Summary 100% home fees covered, and a minimum tax-free annual living