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PhD position on Exposing the Contribution of Epialleles to Heritable Variation Faculty: Faculty of Science Department: Department of Biology Hours per week: 36 to 40 Application deadline: 17
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degree (or equivalent postgraduate qualification) in a relevant discipline, such as Biomedical Science, Biochemistry, Cell Biology, Molecular Biology, Pharmacology, Toxicology, Environmental Science
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This PhD would suit a motivated candidate with a strong background in sport and exercise science, exercise physiology, biomedical science, human physiology or a closely related field. Previous experience in
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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships provide funding for fees and stipend at the rate set by the UKRI, as well as other research training...
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The GW4 BioMed3 Doctoral Landscape Programme is offering up to 18 MRC funded studentships across a range of biomedical disciplines, with a start date of October 2027. These four-year studentships provide funding for fees and stipend at the rate set by the UKRI, as well as other research training...
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resilience in complex and dynamic environments. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering
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). Working in the Faculty of Health and in partnership with Carl Zeiss AG and the ZEISS Innovation Hub, the successful candidate will gain interdisciplinary training spanning clinical vision science
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of the most dynamic environmental conditions. Understanding how these offshore aquaculture systems respond to waves, currents, and extreme storms is one of the key engineering challenges of the next
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Research theme: AI for Engineering How to apply: uom.link/pgr-apply-2425 This 3.5-year PhD project is fully funded; students who are eligible to pay tuition fees at the Home rate are eligible
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an interdisciplinary program that brings together Warwick Medical School and partners from the School of Engineering and Industry, combining machine learning and computational modelling to solve a