323 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" positions at University of Oxford
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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 03 March 2026. Interviews will take place in March 2026 and will be face-to-face or online where
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Coordinator to join the Learning Delivery and Experience (Student and Academic Success) division within the innovative Oxford Saïd Online (OSO) team. OSO is the online education initiative of Saïd Business
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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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Scholarships (https://www.ox.ac.uk/clarendon). The University is committed to expanding the number of scholarships available for outstanding postgraduate students, and the Clarendon Fund’s growth means we
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would like to do this role. See guidance at https://www.jobs.ox.ac.uk/cv-and-supporting-statement. Any technical questions related to this vacancy can be sent to: recruit@ouce.ox.ac.uk The closing date
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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://www.jobs.ox.ac.uk/cv-and-supporting-statement. Any technical
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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 3rd March 2026. It is
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capabilities, and particularly the Experimental Medicine Clinical Research Facility (EMCRF) (https://www.kennedy.ox.ac.uk/news/oxford-and-gsk-launch-experimental-medicine-collaboration). This will allow testing
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3.5-year D.Phil. studentship Supervisors: Prof Noa Zilberman The training of new AI models, as well as their deployment for inference, is transforming the design of computer networks. In