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postholder will hold a PhD (or equivalent experience) in a relevant field, with strong knowledge of quantitative and qualitative research methods, including qualitative interviewing, data analysis using
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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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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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software packages. They also must have demonstrated knowledge of longitudinal data analysis methods, and an understanding of sample size calculations. Further particulars are included in the job description
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epidemiological or econometric methods, using R software package, and an understanding of techniques used in agent-based modelling. The post is full-time 35 hours per week, 1.0 FTE and fixed term until 31 December
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the study in collaboration with research teams at Nottingham, Oxford, UKHSA and Manchester with expertise in mixed-method policy evaluation, antimicrobial resistance, pharmacy practice research, primary care
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mitigation priorities for the UK. Candidates should also be experienced in conducting quantitative research and applying spatio-temporal epidemiologic methods, ideally to environmental health data. Further
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, the ability to supervise students and mastery of written and oral English communication. The successful applicant will have, or be about to obtain, a relevant PhD degree in Mathematics or Physics, or equivalent
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of working in travel medicine and migrant health, experience in a formal teaching environment in higher education and possess excellent digital skills relevant to online education. Further particulars
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should hold a PhD in psychology or a closely related discipline. They must have outstanding quantitative skills, including extensive experience in the design of psychological experiments, and in data