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LSBU is offering over 80 Chancellor’s PhD Scholarships which offer full-time fee waiver for four years. The Scholarships are offered across three colleges: the College of Health and Life Sciences
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and establish their potential remain to be answered, both in terms of enabling technologies for distributed sensing and the techniques that exploit it. The focus of the PhD project is to improve our
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) Applications are invited for a three-year PhD studentship. The studentship will start on1st Jan, 2026. Project Description Glioblastoma (GBM) is the most aggressive and treatment-resistant form of brain cancer
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) change tread pattern (or tread groove depth), ii) aerodynamics by changing the surface texture of sidewall, iii) noise cancelling (vibration), iv) mechanical properties (hardness). Resources: The PhD
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There is an exciting opportunity for two students to pursue a funded PhD with Angelo Pirrone, Nathan Evans, Giovanni Sala and Konstantinos Tsetsos at the University of Liverpool, starting in October
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at highest risk of kidney function decline, aiding trial prioritisation What you will learn and why it matters This PhD provides a rare skill set sought after in academia, biotech, and healthcare innovation
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/people/nc266.html and here https://www.eng.cam.ac.uk/news/engineering-better-car-experience . Four years funding is provided by the UK's Engineering and Physical Sciences Research Council (EPSRC) and JLR
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Project details: Iron is crucial for athlete health and performance, yet iron deficiency is a common diagnosis in active populations. Iron deficiency can hinder haematological adaptations and reduce the body’s capacity for oxygen transport. Additionally, iron plays a vital role in energy...
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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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needs. While muscle imaging from well-characterised patients and transcriptomic technologies provide rich data, these remain under-utilised for predictive modelling. Using machine learning, this project