126 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" research jobs at University of Oxford
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referees as part of your online application. Please see the University pages on the application process at https://www.jobs.ox.ac.uk/application-process The closing date for applications is 12:00 midday
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to undertake analyses of neuroimaging measures, apply machine learning approaches to clinical and neuroimaging data, the development of clinical services, the conducting of meta-analyses, and the analysis of pre
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on the application process at https://www.jobs.ox.ac.uk/application-process The closing date for applications is 12.00 midday 7 April 2026
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area. You will possess sufficient specialist knowledge in the discipline of neurodiversity to work within established research programmes. An experience in engaging in Human-Computer Interaction (HCI) based
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@cardiov.ox.ac.uk Georgia Broom – cvm_personnel@cardiov.ox.ac.uk The University of Oxford offers an attractive range of competitive benefits available to all staff for both work and personal life - https
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statement, CV and the details of two referees as part of your online application. Please see the University pages on the application process at https://www.jobs.ox.ac.uk/application-process The closing date
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opening in March 2026. It will recruit 60 patients with Treatment Resistant Depression, who will have three research visits, involving computer tests focused on reward processing and collecting voice
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of competitive benefits available to all staff for both work and personal life - https://hr.admin.ox.ac.uk/staff-benefits For informal enquiries about the post, please contact Ellie Tzima – ellie.tzima
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application. In your supporting statement, please explain how you meet each of the selection criteria found in the job description, and why you would like to do this role. See guidance at https
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Microbiology (MMM) Unit at the John Radcliffe Hospital in Oxford. (https://www.expmedndm.ox.ac.uk/mmm). The postholder will lead on developing and undertaking workflows to phenotypically characterise bacterial