19 phd-computational-mechanics Fellowship positions at University of London in london
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Applications are invited for this PhD training programme to commence in September 2026. Led by the London School of Hygiene & Tropical Medicine, this PhD Programme is offered by five UK and six
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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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the College’s small animal referral hospital by further developing and delivering advanced cardiac surgical therapies through the open heart surgery programme, at the Royal Veterinary College. We are looking
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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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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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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 experience in computer-based statistical analysis and presentation of results, demonstrated proficiency in a coding language used for data analysis, such as Python or R, strong quantitative skills
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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