491 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UNIV" "Univ" PhD scholarships in United Kingdom
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desirable. Entry requirements At least UK equivalence Bachelors (Honours) 2:1 or UK equivalence Masters degree. Mode of study Full-time Start date 1 October 2026 Additional Funding Information This project is
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and in-depth information about their vision. This could lead to a paradigm shift for monitoring vision in patients with MS – enabling earlier and more reliable detection of vision-related manifestations
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and partner urban sites, the successful candidate will collect and analyse real-world environmental data to evaluate how vegetation interacts with the built environment. You will be supported by a
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for fuel system applications. While these methods provide a wealth of knowledge and information, they remain impractical for industrial use. Therefore, AI modelling techniques will be harnessed to develop
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criteria: Knowledge of the molecular basis of ageing, muscle physiology, or the circadian clock. Experience or strong interest in data science, machine learning, or bioinformatics. Exposure to high
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Dalton Building at Manchester Metropolitan University. You will develop skills in computational experimentation, financial data analytics, and systemic risk modelling, with extensive opportunities
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fees. See UKCISA for further information. Starting in October 2026. Later start dates may be possible, please contact Dr Lucia Florescu once the deadline passes. You will need to meet the minimum entry
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adoption is limited by inconsistent performance data, insufficient understanding of long‑term behaviour, and a lack of standardised testing. This project will investigate new technological pathways
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programme. Nationality restrictions Other eligibility criteria The proposed research project must be within the remit of EPSRC's research themes. Please see EPSRC website for information. Application
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computer simulations by developing fundamentally innovative and advanced protection strategies. To enhance the reliability and safety of low-voltage networks with a high penetration of power-electronic