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assess potential health inequalities. Facilities The PhD student will be based in Population Data Science at Swansea University with visiting PhD Student Status at the Department of Statistics
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that the candidate receives high-quality guidance to enable successful completion of the workplan. This is a novel and impactful PhD, which, will enhance the reputation of the school and university. An internship may
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. Required skills include data analysis, proficiency in statistical and computer tools, climate modeling, risk modeling, and literature research. In terms of soft skills, autonomy, critical thinking
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Award Summary This PhD studentship is part of the National Institute for Health and Care Research (NIHR) Patient Safety Research Collaborative (PSRC). The aim of this collaborative is to deliver
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. The student will benefit from Exeter’s expertise in Statistics, Data Science, and Machine Learning, and be part of a vibrant community of bright PhD students working on environmental problems. The student will
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Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing application date: 10 June 2024 Start date: July 2024 or as soon as
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deficits, such as those caused by glaucoma. You will make use of experimental and theoretical techniques, such as sensorimotor analysis and Bayesian statistical modeling, to create diagnostic and screening
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related to staff position within a Research Infrastructure? No Offer Description Overview Qualification type: PhD Subject area: Control and Machine Learning Location/Campus: College Lane, Hatfield Closing
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University of Georgia Warnell School of Forestry and Natural Resources | Athens, Georgia | United States | 2 months ago
Job Type: PhD Research Assistantship (towards a PhD degree in Water Resources) Project: TEAROOM (Tracer-EnABled hydRolOgic Modeling) Research Focus: Utilizing tracer data for improving hydrologic
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related to staff position within a Research Infrastructure? No Offer Description We are seeking a PhD researcher to (1) evaluate the contribution of innovative biodiversity recovery measures at university