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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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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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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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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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(Southampton and Denmark), and participation in large-scale testing in Denmark to validate industrial applications of your research. This project is an excellent opportunity for candidates who are interested in
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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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on what Large Language Models have told them, and content created by archives is a part of what is used to train LLMs. LLMs have biases presenting problems in dealing with sensitive historical material, and
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Project title: Privacy/Security Risks in Machine/Federated Learning systems Supervisory Team: Dr Han Wu Project description: In the wake of growing data privacy concerns and the enactment