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Applications are invited for a fully funded PhD studentship to be held at the Department of History, University of Warwick and the Royal Geographical Society (with IBG), London. The studentship
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the University of Warwick called ROARS (Revealing Orbital and Atmospheric Responses to Solar activity), recently selected by the European Space Agency. The ROARS consortium features leading scientists and
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the Intel-Merck consortium (the AWASES programme), available as soon as possible (at latest by September 2024). The postholder will work with Dr. Bora Karasulu (University of Warwick, Chemistry Department
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Computer Science - 2 x PhD Scholarships in Interdisciplinary Research Two PhD scholarships are available in the interdisciplinary research programme led by Professor Yulia Timofeeva (Department
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recruits enthusiastic students from across physical sciences, mathematics and engineering who enjoy using their mathematical skills and thinking flexibly to solve complex problems. By developing these skills
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/ . If this initial application succeeds, we invite you to apply for study formally. All candidates must fulfil the University of Warwick entry criteria and obtain an unconditional offer before commencing enrolment
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Supervisors: Prof. James Kermode (Engineering) and Dr. Thomas Hudson (Maths) Summary: Inverse problems are a general class of problems that involve calibrating the parameters of a model using
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Supervisors: Supervisors: Dr. Albert Bartok-Partay (Physics), Prof. James Kermode (Engineering), Dr Livia Bartok-Partay (Chemistry) Summary: In high-performance applications such as aerospace and
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; Midlands Graduate School Doctoral Training Partnership | Nottingham, England | United Kingdom | 3 days ago
Partnership (DTP). One of 14 such partnerships in the UK, the Midlands Graduate School is a collaboration between the Universities of Warwick, Birmingham, Nottingham, Aston Leicester, Loughborough, De Montfort
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. The need to predict the transition state structure as input for quantum chemical barrier predictions adds further complications. Machine learning (ML) models of quantum chemistry can achieve fast and