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associated genes, which together with the increased distribution of type I (slow twitch) fibres increases resistance to fatigue. However, the mechanisms by which these differences occur are not fully
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. Extended periods of fieldwork are anticipated. Primary supervisor: Dr Danny Longman Entry requirements: Essential criteria: 1. Undergraduate honours degree in a related subject area (e.g., Health Sciences
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that advance humanity, one of Syensqo’s priorities is to invest in a sustainable future. The PhD student will join the ‘Advanced Nanomaterials Group’ under the supervision of Dr Cristina Vallés and Prof Ian
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of carbon steel infrastructure, such as pipelines, remains limited. One popular theory to explain localised corrosion relates to the macroscopic differences in surface condition on the internal pipeline wall
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. Supervisors Primary supervisor: Georgios Mavros Secondary supervisor: James Knowles Entry requirements Applicants should have, or expect to achieve, at least a 2:1 Honours degree (or equivalent) in Engineering
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One PhD studentship is available in the area of machine learning theory (statistical learning theory and deep learning theory) or theoretical-oriented topics, e.g., trustworthy machine learning
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hazards and environmental challenges. In this project we will exploit breakthroughs in one of two connected areas, according to the expertise of the student: Development of nanoscale physics-based
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relevant skills as well as the opportunity to work with a industry for direct application of the work. Applicants should have achieved a minimum 2:1 honours BEng in a relevant engineering field (Mechanical
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-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline (physics, mathematics, computer science, AI, data science or statistics). Strong candidates with sports science
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OF THE PROJECT Essential Criteria: A first-class or upper second-class (2:1) degree (or equivalent) in a relevant discipline such as mathematics, computer science, AI, data science or statistics. Experience in