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Honours degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work
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-informed machine learning (PIML) with domain-specific engineering knowledge. By embedding physical laws and corrosion mechanisms into data-driven models, the research will produce more accurate
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trials on clinical practice in the UK. The successful applicant will be supervised by academics from the Centre for Evidence and Implementation Science (CEIS), University of Birmingham (Professor Amy Grove
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degree or a Master’s programme and/or have worked for some years; or are currently enrolled in a PhD programme overseas Upon successful completion of the PhD programme, the scholar will be considered
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30 Oct 2025 Job Information Organisation/Company The University of Edinburgh Department School of Engineering Research Field Engineering » Civil engineering Engineering » Geological engineering
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machine learning. The position will involve working with different research groups in the Department of Computer Science at the University of Cambridge, UK. In this collaborative project, we will apply
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equivalent in a subject relevant to the proposed PhD project (inc. computing, mathematics, engineering etc.). Enthusiasm for research, the ability to think and work independently, excellent analytical skills
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: Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in Geography, Environmental Science, Computer Science, or Engineering. A relevant master’s degree and/or experience
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undergraduate degree relevant to each individual project, e.g., Experimental Psychology, Physics, Neuroscience, Computer Science, Electrical Engineering, or a related subject. In addition, an appropriate master’s
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of Edinburgh and will be jointly supervised by: Dr Dominik Leichtle, School of Informatics, University of Edinburgh Dr Elham Kashefi, School of Informatics, University of Edinburgh Dr Ivan Rungger, National