13 phd-in-mathematical-modelling-population Fellowship positions at University of London
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extracellular histone, a damage-associated molecular pattern that is implicated in adverse outcomes after injury. This project will include using a range of clinically relevant models as well as in vitro
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the use of mathematical modelling of infectious diseases. The post-holder will work closely with partners within the Global Polio Eradication Initiative to ensure that research is focused towards supporting
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, in a relevant topic and relevant experience with mathematical modelling of infectious diseases. Strong knowledge of a programming language (e.g. R, Python) is essential. Experience in mathematical
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health researcher is sought with expertise in quantitative surveys, mathematical modeling in nutrition (especially in Sudan), and crisis clinical nutrition program management. The ideal candidate should
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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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, 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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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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, 1.0 FTE and fixed-term until 31 December 2025. The post is funded by the Faculty of Epidemiology and Population Health (EPH) and is available immediately. The salary will be on the LSHTM salary scale
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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