368 data-"https:"-"https:"-"https:"-"https:"-"https:"-"RMIT-University" PhD positions in United Kingdom
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the ergogenic aid. Further information: Applicants should have at least a 2:1 in a relevant degree (for example but not limited to Sport Science/Medicine/Rehab, Public Health, Medicine), and ideally a relevant
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completed application should be emailed to the Doctoral College Admissions team by 2nd March 2026 at 9am. Funding information Full tuition fee waiver for three years and a stipend each year at UK Research
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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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training will be provided. We particularly welcome applicants who are excited about integrating ecological understanding with data-driven methods. There is flexibility to tailor the research to your
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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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and experimental data, the student will develop predictive models of strand displacement reactions in non-ideal settings, in collaboration with the other students within BIOHYBRITE. These areas include
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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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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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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