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The primary objective of this project is to establish the evidence base on professional cycling road ‘racing’ trends and the critical tactical moments that determine how races are won. This evidence will inform future race strategy and live race tactics. Multiple factors influence the strategy...
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Cranfield University invites applications for a PhD funded by Thames Water through the Ofwat Innovation Fund. The studentship covers full Home tuition fees plus a tax free stipend of £24,000 per
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Are you fascinated by working at the intersection of physics and synthetic biology? This PhD project offers a unique opportunity to develop autonomous microswimmers, which are bioinspired structures
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Sh.24.3 PhD in Sound Analysis for Predicting Category 1 Ambulance Calls School of Computer Science PhD Research Project Competition Funded UK Students Dr Ning Ma, Prof Jon Barker Application
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3 Year, full-time funded PhD Studentship Eligibility: Home UK/EU and International applicants Bursary p.a.: Bursary equivalent to UKRI national minimum stipend plus fees (current 2025/26 bursary
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, with a slightly higher teaching requirement alongside the PhD studies and would particularly suit someone with an interest in future education as well as research, such as academia. Supervisors:Professor
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fully funded PhD research studentship in Physics-Informed Machine Learning for Cardiovascular Medicine. This opportunity is open to UK (Home) candidates only. Project Overview Arrhythmias are disorders
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, the race is on to understand how these DNA changes affect disease progression and post-operative recurrence risk. This PhD project uses CRISPR/Cas9 to examine risk variants in patient-derived cells and
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mechanisms , smart electroactive materials , embodied intelligence , advanced control systems , and microfabrication techniques . This PhD forms part of the new £14 million VIVO Hub for Enhanced Independent
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, and space hardware. This PhD research aims to develop a comprehensive Mode Selection Framework for Reduced Order Modelling (ROM) in Structural Dynamics—using machine learning to build robust