59 computer-science-intern-"https:"-"https:" PhD positions at University of Nottingham
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at the interface of materials science and power electronics. Applicants should hold, or expect to achieve, at least a 2:1 degree (or international equivalent) in a relevant subject (e.g., Materials Science
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, robotics, and machine learning. You will work within a multidisciplinary supervisory team spanning engineering, robotics, and computer science, and collaborate with researchers working on real-world
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centre for the development of medical imaging, particularly MRI. Eligibility You must be a university graduate or expecting to graduate with a 1st class degree in engineering, physics, computer science
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or computational frameworks, developed in collaboration with partners with modelling expertise. This PhD offers the opportunity to work at the interface of plant physiology, root biology, imaging and quantitative
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science, 3D printing, biotechnology, and engineering. Eligibility and Application This call is open to UK-Home students and to International students. Project start date and duration: Start October 2026
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will equip you with skills in materials science, 3D printing, biotechnology, and engineering. Eligibility and Application This call is open to UK-Home students and to International students. Project
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computational solid-state physics/ chemistry. Candidates with experience in the synthesis, the characterisation and performance testing or modelling of metal hydrides, complex hydrides and/or their composites
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, Computer Science and the Biosciences. You will be supervised by Amanda Wright (Optics and Photonics Research Group, Faculty of Engineering), Mike Somekh (Optics and Photonics Research Group, Faculty
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of Sport, Exercise, and Nutrition Education – kimberley.edwards@nottingham.ac.uk This project is not funded, and we are seeking a student who can self-fund the PhD. Programme description: Athletes, coaches
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engineering excellence needed for the aerospace sector. In this PhD, high-fidelity two-phase Computational Fluid Dynamics (CFD) methods will be used to model complex and fundamental cryogenic hydrogen flows