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postgraduate student learning through their specialty discipline. We are looking for candidates with an RCVS registrable degree in veterinary science, who also hold or are eligible to sit an American or European
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postgraduate degree, ideally a PhD, in statistics, machine learning, or a related field. Experience of developing new statistical methods and a strong working knowledge of a statistical software package, such as
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independently and in close collaboration with in-country partners. The applicant should have an excellent academic track record that includes formal training in microbiology as well as a relevant PhD (public
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relevant PhD and medical degree alongside registration with the GMC at Specialist Registrar Garde or below. You will have a recent track record in histopathology and use of machine learning techniques, and
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of the research project and develop new areas of research. The post-holder will be expected to undertake a higher degree such as a PhD during the fellowship. About You The applicant must be a medically qualified
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degree, ideally a PhD, in health economics, medical statistics, data science, epidemiology or a related field. A clear conceptual understanding of causal inference methods such as instrumental variable
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will have a PhD in a related field, an emerging track record of outstanding publications, and well-developed plans for new research projects. This post is generously funded by the A. G. Leventis
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PhD) while conducting highly policy relevant research. Applicants should have a postgraduate degree with MRCP or MRCS. Relevant clinical experience in providing cancer treatments, co-ordinating clinical
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, Professors Ruth Keogh and Kate Walker. Applicants should have a postgraduate degree, ideally a PhD, in medical statistics, epidemiology, health economics or a related field. Relevant experience in applying
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analytic codes to investigate the benefits and harms of medications. Candidates must have a doctoral degree (or be within 3 months of anticipated completion of a PhD) in medical statistics or epidemiology