30 phd-position-computer-science-"IMPRS-ML"-"IMPRS-ML" Fellowship positions at University of 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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for a Research Fellow in Bioinformatics/Computational Biology to help develop, coordinate, and conduct robust analysis of high-throughput host protein data under supervision using advanced analytical and
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Science and Services. You will be expected to enhance the department’s reputation through scholarship in clinical activities and teaching. You will achieve this by delivering professional services within
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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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fellow position within the William Harvey Research Institute at Bart’s and The London Medical School, Queen Mary University of London (QMUL). The post-holder will work on projects including the PinG study
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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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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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addressed to jobs@lshtm.ac.uk . Please quote reference EPH-DPH-2025-08-R. Informal queries about the position can be directed to Chido Dziva Chikwari, SRHPP Programme Director (chido.dzivachikwari@lshtm.ac.uk
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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