64 computational-"https:"-"https:"-"https:"-"https:"-"https:" positions at University of Sheffield
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simulations to translate computational predictions into testable experimental hypotheses and vice versa. Disseminate research outputs through publications in international peer-reviewed journals, oral and
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Improving the clinical success of porous orthopaedic designs – experimental and finite element study
Improving the clinical success of porous orthopaedic designs – experimental and finite element study School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded UK Students Dr Vee San Cheong, Dr P Fromme Application Deadline: 30 November 2026 Details Bone cancer...
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ramifications for the future of nuclear power. A successful and greatly expanded nuclear power programme is needed to meet the ever-increasing energy demands from AI data centres as well as meeting the legally
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support. Build a close working relationship with the Regional Engagement Team, ensuring that you understand the priorities of our Regional Engagement Work Programme and how your team can deliver against
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focused KE support. Build a close working relationship with the Regional Engagement Team, ensuring that you understand the priorities of our Regional Engagement Work Programme and how your team can deliver
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through their programme. The ideal candidate will have outstanding customer service and interpersonal skills, and an ability to put people at ease through sophisticated and sensitive verbal and non-verbal
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the University externally on panels and committees relevant to PGR students. Actively promote the successes of PGR students, supervisory and/or programme teams both within the University and more broadly, to
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Strathclyde. The ethos of the programme is to recruit students from across STEM and give them the necessary skills and training to become a subject matter expert in the nuclear sector in either industry
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Overview The Engineering and Maintenance team provide a reactive and planned programme of works for every building within the university, working in collaboration with other university departments
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Reliable and Efficient Adaptation of Large Language Models