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investigate the “Solar wind outflow and open flux”. Candidates with a strong track record in solar magnetic field modelling, and/or familiarity with solar observational data, are particularly encouraged
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provision. The Role A Postdoctoral Research Associate position is available to pursue experimental research at the intersection of semiconductor and atomic physics within the Durham Quantum Light and Matter
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implementation of deep learning and computer vision frameworks across a range of research projects. This includes developing and training deep learning models for tasks such as scene understanding, object
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and PhD students. Research spans a wide range. Current interests include: Bayesian statistics; modelling of structure, geometry, and shape; statistical machine learning; computational statistics; high
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place to work, a unique and historic setting – Durham is a university like no other. As one of the UK’s leading universities, Durham is an incredible place to define your career. The University is located
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area on our Why Join Us? - Information Page The Role and Department The successful candidate for this Research Associate position will join the Department of Engineering at Durham University. This is a
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pursuing a PhD at Durham University. Flexible working arrangements are available for this position. Key responsibilities: Working closely with the Principal Investigators (PIs) and Co-Investigators (Co-Is
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University A globally outstanding centre of teaching and research excellence, a warm and friendly place to work, a unique and historic setting – Durham is a university like no other. As one of the UK’s leading
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excellence, a warm and friendly place to work, a unique and historic setting – Durham is a university like no other. As one of the UK’s leading universities, Durham is an incredible place to define your career
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to the reach and scope of the Contextual Safeguarding programme most team members work to a hybrid working model, with research underway in various part of the UK as well as via online data collection methods