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, including Engineering, Physics, Mathematics, and Computer Science – candidates with experience with Machine Learning and/or Crystallography would be particularly suited for this project. This project will
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such as Geography, Computer Science, Planning, Statistics, Mathematics, Physical Sciences, Social Sciences are likely to be relevant. Or other relevant or equivalent qualifications. They will also have
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mathematics. We have a world wide reputation for academic research with consistent top research ratings. The Department has an open and collaborative culture, supporting revolutionary fundamental
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, engineering, mathematics or computational, have experience in the use of biomedical/clinical imaging data, have proven experience in programming (Python preferably) for medical imaging processing and machine
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of Mechanical Engineering’s Thermofluids Group at the University of Sheffield for a fully-funded PhD on mathematical modelling and control of turbulent flows. Working under the supervision of Dr Elena Marensi
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diversity in Science, Technology, Engineering and Mathematics (STEM) through our Athena SWAN Bronze award and action plan, we are members of the Women’s Engineering Society (WES) and Working Families, and
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level in a discipline related to Engineering, Physics, Mathematics or Computer Science and have obtained or about to obtain a PhD in the same area with application to deep learning. You will have
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Novel topologies and technologies for advanced reluctance machines Key information Lead supervisors: Dr Pedram Asef Application deadline: 15 June 2024 Project start date: 01 October 2024 Project duration: 4 years Studentship funding: Full Home/UK tuition fees (currently £6,035/year) and...
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. Supervisors: Dr Ben Wetenhall Eligibility criteria Applicants should have a minimum of an Upper Second Class Honours degree in an Engineering subject, Physics or Applied Mathematics with a good understanding of
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universities in the UK, having an excellent teaching quality profile, strong performance in third stream income generation and an improving REF performance. The Department of Computing and Mathematical Sciences