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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
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, machine-learning tools, and Lagrangian transport modelling. You will be based at the British Antarctic Survey and work closely with experts at the University of Leeds and Exeter, who provide cutting-edge
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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for computer, lab, and fieldwork costs necessary for you to conduct your research. There is also a conference budget of £2,000 and individual Training Budget of £1,000 for specialist training Project Aims and
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qualification/experience in statistics, bioinformatics, computer science, computational biology, genomics, or a related discipline. They will have experience applying statistics and/or machine learning
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a much stronger focus on experiential learning. This includes significant amounts of small group task-based working and research projects as part of the curriculum. About you You will: Possess
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-quality research outputs. You will be to support the design, delivery and production of teaching and learning material for a range of core postgraduate modules within our MA Education programmes. You will