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Field
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analysis of data from a Nipah virus vaccine trial, using machine learning and statistical tools to identify immune response markers for future trials. You will be responsible for developing new and adapting
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available option. Applicants with a range of academic subject backgrounds are welcomed, including natural sciences, engineering, statistics and applied mathematics with experience and/or growing interest in
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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research programme at Oxford. Candidates should hold a PhD in biomedical engineering, computer science, medical physics, statistics, or a related field. A strong track record of first-/senior or co-author
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of mole activity and soil health and biodiversity, collecting data on visitor perceptions of moles and their management, and analysing findings using statistical modelling approaches. The role provides
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knowledge and hands-on experience in data collection, secure data storage, and statistical analysis, adhering to open science practices and data protection regulations. Understanding of Randomised Controlled
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, computer science, statistics, or a related field together with strong programming skills in Python, R, or similar languages, and proficiency in high-performance computing. You will have experience in large-scale
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well as statistical inferences on model outputs, for pre specified research hypotheses. Suggesting further appropriate methods and analyses. Writing manuscripts in to disseminate research findings. Present summaries
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the development of study protocols in collaboration with PPIE representatives for ethical review submission. Strong knowledge and hands-on experience in data collection, secure data storage, and statistical
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show evidence of understanding, expression and application of concepts and methods. Strong data analysis skills, especially in applied econometrics and statistical methods within health economics are