17 phd-studenship-in-computer-vision-and-machine-learning Fellowship positions at University of London
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have a postgraduate degree, ideally a doctoral degree, in a relevant field and experience in computer-based analyses and presentation of experimental data. The successful applicant would have a proven
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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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, including seeking opportunities for mutual learning and training. The successful applicant will have a track record of relevant research experience, excellent communication skills, and an ability to work both
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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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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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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