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. For candidates interested in research beyond the current post, there may be opportunities available to develop a programme towards a higher degree (PhD) in an associated topic. You will be working in a growing
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fellowships are funded by the British Council under the UK’s International Science Partnerships scheme. The fellowships programme aims to provide the selected early career researchers with a strong foundation
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uncertainty quantification via conformal prediction. You’ll contribute to open-source projects, extending tools like MONAI for the computational pathology community, and support the data science team in
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to) fundamental research in machine learning or statistics, algorithm design, the application of AI methods in science, healthcare, social sciences, or business. You should have a PhD or equivalent level of
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. You will also be responsible for implementing the model as a computer simulation and analysing it within a health-economics framework using standard computational techniques. You will also be
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/Fellow who can deliver the research whilst helping to manage project delivery. Candidates must hold an appropriate social science degree level qualification and a PhD (or be about to obtain a PhD, which
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uncertainty quantification via conformal prediction. You’ll contribute to open-source projects, extending tools like MONAI for the computational pathology community, and support the data science team in
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sponsorship under UK Visas and Immigration legislation. About you The ideal candidate will hold a PhD or equivalent experience in a quantitative field relevant to the research focus, such as economics, data
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become available in the Faculty of Health and Medical Sciences, in collaboration with the Surrey Institute for People-Centred Artificial Intelligence and the School of Mathematics at the University
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for applicants that demonstrate strong research in one or more of the above areas backed by a strong publication record. Applicants must have or be close to obtaining a PhD in Computer Science. How to Apply Please