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diagnosis of psychosis. The postdoctoral researcher will lead a research program focused on developing and testing the computational mechanisms of social inference, although will have plenty of scope, and
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(PDRA) with experience of statistical analysis and/or modelling, computer programming and preferably some meteorology/climate background, to join our growing team. You will work directly on the NERC
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37 Faculty of Business, Economics and Statistics Startdate: 01.10.2025 | Working hours: 40 | Collective bargaining agreement: §48 VwGr. B1 lit. b (postdoc) Limited until: 30.09.2028 Reference
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subject area for the research programme is essential. Demonstrable experience in computational biology, computer science, statistics or related field. Essential experience in scripting languages to support data
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and present the results at national and international conferences. It is essential that you hold a PhD/DPhil in computational biology, genomics, bioinformatics, computer science, statistics, or a
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Putts; and inflammatory genetics. The successful candidate will support other researchers and MSc/PhD students. They will develop novel scientific questions and lead on the analysis of specific aspects
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links between online polarization and a multidimensional perspective on mental health and wellbeing. Using advanced methods in statistical modeling, NLP, HCI, and experimental design, it will also test
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with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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Postdoctoral Researcher: Oxford Centre for Emerging Minds Research ‘All About Me’ (Adolescent Focus)
with an international reputation for excellence. The Department has a substantial research programme, with major funding from Medical Research Council (MRC), Wellcome Trust and National Institute
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Experience in statistical or scientific programming (ideally R and/or Python) Experience in analyzing large and/or complex datasets Interest in quantifying uncertainties for computer models and/or climate