198 data-"https:" "https:" "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" "UCL" research jobs 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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data collected within the clinic to be used for research purposes, by securing relevant permissions, developing relevant protocols and procedures, and supporting the implementation of these procedures
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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 for applications is
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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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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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these and to deduce fluxes of heat and material across the MTZ. The post holders will be responsible for compiling seismic data sets (focusing on surface-wave overtones and body-wave data including precursors
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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