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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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Computer Science or a related topic. Applicants at the PDRA level must have a PhD in NLP or machine learning. Substantial knowledge of Natural Language Processing (NLP) and machine learning methods is essential, as
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researcher with experience in participatory methods and community engagement. You'll have a degree in a relevant field and experience in qualitative and quantitative research methods. Experience working with
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the successful candidate embarking on a PhD programme at LSHTM. It is anticipated that the role will lead to a further 18 month funded opportunity at Max Planck Institute for Demographic Research (MPIDR), in
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Product (CTIMP). TiLLI-High aims to investigate the most effective and cost-effective method of pharmacological prophylaxis in a population at higher risk of VTE. TiLLI-Low aims to investigate
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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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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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environment, home to 450 staff, 100 PhD students and 500 postgraduate taught students. It harnesses expertise across a wide range of population-based research and education activities and is an internationally
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supervised by Dr. Damien Tully and applicants are encouraged to contact Dr. Tully (Damien.Tully@lshtm.ac.uk ) for an informal discussion prior to submitting a formal application. The post-holder will have a
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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