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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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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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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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). This global project aims to assess the behavioural and social drivers of vaccine uptake and to identify both supply- and demand-side barriers to immunisation. The post-holder will contribute to modelling
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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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to join the Environment & Health Modelling (EHM) Lab (https://www.lshtm.ac.uk/ehm-lab ) led by Prof Antonio Gasparrini. The successful candidate will work on the project CONNECT – Cohort and environmental
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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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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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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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research project on cardiovascular risk prediction for people with immune-mediated inflammatory disease. The successful candidate will use advanced risk prediction methods to develop prediction models