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Florence nightingale bicentenary fellow in statistical machine learning

Oxford
University of Oxford
Posted: 9 March
Offer description

Department of Statistics, 24-29 St Giles, Oxford, OX1 3LB The Department of Statistics is recruiting to a Florence Nightingale Bicentenary Fellow in Statistical Machine Learning. This is career development positions intended to carry around half the teaching load of an ordinary Oxford faculty position. The successful candidates would be expected to take up their role in September 2026. Funding for research costs of £2,000 per year will be available. The post holder will join the dynamic and collaborative Department of Statistics. The Department carries out world-leading research in computational statistics, machine learning, theoretical statistics, and probability as well as applied statistics fields, including statistical and population genetics, bioinformatics and statistical epidemiology. The successful candidate will hold a doctorate in the field of machine learning, statistics, or a related subject. The Department seeks candidates with research interests that integrates well with research by current members of the department and lies within the area of statistical machine learning (defined in the broad sense of either principled, probabilistic, or foundational research in machine learning, or the use of machine learning techniques for statistical problems like estimation, inference, and experimental design). We aim to hire a candidate who can teach and supervise projects in statistical machine learning and/or other mainstream topics in statistics (for example, applied and computational statistics, simulation and statistical programming). All successful candidates will be outstanding individuals who have the potential to become leaders in their field. They will have the skills and enthusiasm to teach at undergraduate and graduate level within the Department of Statistics, to provide pastoral care to students, and to supervise MSc student projects. They will carry out and publish original research. This post is fixed term for 3 years.

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