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4-year PhD fellowship in the Research Programme - Deep Learning-Accelerated Crystallography Pipeline
qualification equivalent to a Master’s degree in Chemistry, Mathematics or Computer Science by the start of the PhD; A curious mind-set and strong background in quantum crystallography and its underlying
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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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guidance, research training, publication support, hardware/compute access and Intel engagement opportunities. Entry requirements: Relevant undergraduate or master’s degree in computer science, AI
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range of stakeholders. Desirable Criteria: A PhD (or be close to submission) in Civil/Mechanical Engineering or a related subject. Experience of conducting high quality academic research. Experience
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), alongside joint honours degrees with Philosophy and Economics, and a PPE programme. We also contribute substantially to Combined Honours in Social Sciences and Liberal Arts pathways. At postgraduate level, we
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, will contribute to the future success of Durham University’s Institute for Computational Cosmology (ICC). A world-renowned research centre, the ICC is about to celebrate its 25th anniversary during
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Cosmology is one of the leading international centres for research in computational cosmology and astrophysics. We deliver world-leading science using high-performance computing, using it as a foundation to
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public understanding of fundamental science. We also have close ties to colleagues in the Institute for Computational Cosmology, housed in the Ogden Centre West. The Position The Department of Physics
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young people from socially disadvantaged backgrounds into a technical career in environmental science. Part of the NERC ‘Opening up the Environment 2026’ work programme, the project seeks to lead and
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physics, cosmology, particle physics and quantum physics. It has real world impact, informing developments in energy storage, improved cancer imaging, quantum computing, nuclear fusion, pandemic modelling