570 computer-science-intern-"https:"-"https:"-"https:"-"https:"-"IFM" PhD positions in United Kingdom
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the ‘Sustainable Transitions – Leverhulme Doctoral Training Programme’ at the University of Essex. Only UK domiciled applicants who meet the following criteria may apply: Not already have a Master’s degree from the
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First or strong Upper Second-class honours degree (2:1 with 65% average), or international equivalent, in Engineering, Computer Science, Physics, Mathematics, or a related discipline, and demonstrate
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-edge computational project at the intersection of AI, physics-based simulation, and materials science. Your key objectives will be to: Build a Multi-Scale Training Dataset - You will use 3D X-ray
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for the candidate to have prior training in development studies, government, business or economics as any relevant training will be provided on the programme. Research Proposal The project area is broadly defined
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policies, often facing opposition from internal and external stakeholders. Focusing on either sustainable food or active travel policies, in this PhD you will assess who the influential stakeholders
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district-wide universal offer for children and young people’s physical activity in Bradford. The studentship is embedded within Join Us Move Play, Bradford’s Local Delivery Pilot (LDP) programme , funded by
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master’s (or international equivalent) in a relevant science or engineering related discipline. Graduates from Physics or Mechanical Engineering are welcome. To apply, please contact Prof João Quinta da
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for applicants with a degree in Computer Science, Mathematics, Physics, or Engineering. Prior experience in AI is necessary. Prior experience in tomographic imaging and medical physics would be advantageous but
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Start date: 28/09/2026 Funding Sponsored by Cranfield University, this full-time PhD Studentship will cover the PhD programme fees and provide an annual bursary of £20,780 (tax free) for three years
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CD3 is a new, multidisciplinary and multi-institutional strategic national research programme dedicated to using data to transform our understanding of cancer risk and enable early interception