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protocols for electrical biasing of samples in the microscope. A key task is to process and analyse large 4D-STEM data sets and extract information about domain wall structure and dynamics. The role involves
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quantitative data analyses. The role involves co-ordinating a large study and liaising with clinical participants, so the ideal candidate would have exceptional interpersonal and organisational skills. Further
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theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics and PhD students, and communicate your research at national
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Responsibilities for the role include: Data collection, cleaning, and merging from large-scale microdata sources (e.g., patents, dissertations, bibliometrics). Conduct data analysis using econometric and statistical
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optimization techniques, coding new algorithms, creating new mathematical theory, and the analysis of large data ensembles. You will write papers for submission to academic journals, collaborate with academics
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Applications are invited for a fully funded, full-time PhD studentship in the Department of Mechanical and Aerospace Engineering, supported by Vestas Technology (UK) Ltd, one of the largest wind
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research takes a transdiagnostic approach, focusing on common mental health symptoms and identifying both risk and resilience factors. Using large-scale longitudinal data from over 114,500 participants, we
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part of the wider group of CDP funded students across the UK, with access to events and training delivered in partnership with a range of cultural heritage institutions. Project Overview This PhD project