16 computer-vision-phd Fellowship positions at University of London in United Kingdom
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to improve people's health in developing countries by striving for excellence in research, healthcare, and training. Our research program spans basic scientific research, clinical studies, epidemiological
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About the Project We are seeking a talented and dedicated team of scientists, bioinformaticians and support colleaguesto join the ground-breaking PharosAI initiative – a £43.6M national programme co
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in partnership to achieve excellence in public and global health research, education and translation of knowledge into policy and practice. The Baby Ubuntu programme is a group-participatory programme
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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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(Maternity Cover) to support teaching on the mentorship programme and the evaluation of the online MSc Sexual and Reproductive Health Policy and Programming (SRHPP) which is co-delivered with the University
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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