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background in research in cardiopulmonary medicine or surgery and a PhD, although desirable, are not essential. We offer a generous reward package and benefits including: Competitive and attractive pension
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exciting project that will develop new approaches to handle missing data in statistical analyses based on machine learning methods. The Research Fellow will be based in the Department of Medical Statistics
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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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Research Fellow. The successful candidate will be a medical doctor with experience of providing cancer treatments (radiotherapy or surgery), and will join a new NIHR-funded research project called TACTIC
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hours per week, 0.5 FTE and fixed term until 31 December 2025. The post is funded by the Accountability in Action of Medical Research Council and is available from 01 August 2025. The salary will be
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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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date. Any queries regarding the application process may be addressed to jobs@lshtm.ac.uk . Please quote reference PHP-PHES-2025-12. The supporting statement section should set out how your qualifications