203 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "Washington University in St" research jobs at University of Oxford
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About the role This fixed term, non-renewable, one-year postdoctoral position will support continuing analysis of data from the new London English Corpus, which has been developed as part of
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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 questions related to this vacancy can be
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decay — helping shape the future of conservation through data and digital insight. This is your chance to combine cutting-edge research with real-world impact. You’ll help advance digital twin technology
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quantitative and programming skills along with a track record of designing neuromodulation and neuroimaging studies in healthy participants, of using computer programs to design experimental paradigms, analyse
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using computer programs to design experimental paradigms, analyse data and conduct advanced statistical analysis. You will have excellent communication skills, including the ability to write
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this role. You will contribute to the design of research materials and make arrangements for data gathering, including data from interviews and surveys, while analysing and presenting qualitative and
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operando data related to battery degradation and safety. You will develop and implement advanced deep learning models to analyse multi-modal operando data from accelerated stress testing, with the aim
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recruitment into EPIC-Oxford 2, a new phase of the EPIC-Oxford cohort study, and will work in close collaboration with a Data Manager and epidemiologists from the Unit. The main responsibilities will include
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their host galaxies by overcoming the glare of the AGN that overwhelms the host galaxy’s light. The successful candidate will primarily analyse data from the James Webb Space Telescope. In addition, they will
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to better predict incident disease from high-dimensional genetic and non-genetic data, quantification of trait heterogeneity and asymmetry from imaging data, leveraging biobank genetic and prescription data