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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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lead analyses of large-scale datasets, applying advanced computational and statistical methods to integrate multimodal data (including MRI, MEG, EEG, and genomic data). The postholder will work with a
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a social science discipline (or a relevant data science field), have interest and research in the field of economic and experience in data management and analysis. You have demonstrable experience
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under the Skilled Worker Route. Applicants wishing to consider the SWR must ensure that they are able to meet the points requirement before applying. There is further information about this on the UK
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carry out appraisals. Further information about CTC can be found at www.ctc.cam.ac.uk and the GR Group at www.damtp.cam.ac.uk/research/gr/ . Start date: October 2026 Fixed-term: The funds for this post
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, collaborative research that combines lab-based experiments with large-scale data handling and analysis. The post is available for up to 25 months in the first instance with a preferred start date of July/ August
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immediately until 31st March 2028, with the possibility of extension. The post is part of a Wellcome Trust Collaborative grant ‘Harnessing epidemiological and genomic data for understanding of respiratory virus
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Brassart (email: Laurence.brassart@eng.ox.ac.uk ). For more information about working at the Department, see www.eng.ox.ac.uk/about/work-with-us/ Only online applications received before midday on the 7th
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are passionate about applying machine learning to real-world clinical challenges. The successful candidate will lead the development and validation of predictive models using multimodal data including neuroimaging
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of healthy aging using cutting-edge single-cell and AI-based tools, as well as population cohort data. You will be an integral member of the Awadalla lab, working with some of the world’s largest single-cell