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of Computational Solid Dynamics Building upon very recent work made by the supervisory team, this FULLY FUNDED 4-year PhD project will investigate challenging aspects such as: (1) exploration of a truly “n” multi
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How many positions: 1 Open to Uk students This 3.5 year PhD is fully funded for home students; the successful candidate will receive a tax-free stipend set at UKRI rates (£20,780 for 2025/26) and
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This research opportunity invites self-funded PhD candidates to develop advanced deblurring techniques for retinal images using deep learning and variational methods. Retinal images often suffer
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greater energy intake on days when snacking occurs, positive energy balance and body weight gain (1,2). Snack foods are also typically high in energy density, high in fat, salt and sugar, and low in
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open, reproducible science. This exciting and challenging project will be jointly supervised by Prof Rossiter (Soft Robotics) and Prof Kent (Psychology) but will offer many networking opportunities and
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, networking and career development after the PhD. The Faculty has outstanding facilities and works in partnership with leading industrial partners. Start Date would be 1st October 2025 for 4 years. Contact
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Supervisory Team: Hywel Morgan, Dan Spencer and Franklin Nobrega (Biological Sciences) PhD Supervisor: Hywel Morgan Project description: Antimicrobial resistance is expected to become a primary
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PhD Studentship: Sleep and Circadian Rhythms in Elite Sport (Co-funded by Brighton & Hove Albion FC)
We are seeking an exceptional candidate for a fully funded PhD studentship investigating the role of sleep and circadian rhythms in elite athletic performance, co-funded by Brighton & Hove Albion
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This PhD opportunity at Cranfield University invites candidates to explore the integration of AI into certification and lifecycle monitoring processes for safety-critical systems. The project delves
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As part of the Restoration Ecology and Dynamics (READY) Doctoral Focal Award, we invite applications to the following PhD project: Measurement and modelling of future persistence of restored