606 computational-physics "https:" "https:" "https:" "https:" "Caltech" PhD scholarships in United Kingdom
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of first or second class UK honours degree or equivalent in a related discipline, science (chemistry/physics) or engineering. The ideal candidate should have some understanding in the area of materials
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publication governance. The work also aligns with Surrey’s GAIN programme and games provision within SAHCI, and will support standards-oriented impact through the supervisory team’s engagement with ITU-T work
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the wind/renewable energy training and research elements of the CDT programme. Funded by ESB and EPSRC, this 4 year this PhD studentship, at the University of Strathclyde is in the area of economics and
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multidisciplinary effort to investigate the anatomical, physical and cellular factors that shape internal root environments. The project will explore how root organisation and environmental conditions combine
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programme. The duration of the PhD is 4 years, and the start date is 1st October 2026. The PhD student will be physically located in Manchester within the Henry Royce Hub Building – as part of The Department
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, synthesise and test the compounds. Using traditional methods, the equivalent process would take months. These approaches will be applied to optimisation of drug candidates for selected cancer targets
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in indirect damage inspection is that damage causes changes in physical properties of the structure, which can lead to altering the vibration behaviour of the structure. The challenge in indirect
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A 4-year PhD position is available as part of the EUTOPIA PhD co-tutelle programme, working jointly in the groups of Mark Greenhalgh at the University of Warwick and Frank De Proft and Mercedes
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suitable candidate is found, funding is then sought from the University of Nottingham as part of a competitive process (this will cover home tuition fees and UKRI stipend) The University actively supports
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap