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£19,000 per year. Applications for this PhD studentship should be submitted via the University of Cambridge Applicant Portal by clicking the 'Apply' button above, with Professor Duncan McFarlane identified
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£19,000 per year. Applications for this PhD studentship should be submitted via the University of Cambridge Applicant Portal www.graduate.study.cam.ac.uk/courses/directory/egegpdpeg , with Professor Duncan
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will be based in the Department of Aeronautics at Imperial College London but will also interact with colleagues at the University of Cambridge and at the Edinburgh Parallel Computing Centre. If you
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before the end of 2025. The candidate will be based in the Department of Aeronautics at Imperial College London but will also interact with colleagues at the University of Cambridge and at the Edinburgh
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quantitative analysis skills and experience developing algorithms and/or conducting statistical analyses with biological datasets. Background and work knowledge in statistics, algorithms, optimization of novel
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for the Future of Intelligence (CFI). The candidate will contribute to teaching and lead a research programme in one or more of the following areas: ethical use of machine learning, AI safety, algorithmic
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– International English Language Testing Service. Minimum result: 6.5 Certificate in Advanced English (CAE) or Certificate of Proficiency in English (CPE) from the University of Cambridge PTE Academic – Pearson
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, at the University of Cambridge, UK. The Research Assistant will work together with a team of students and research collaborators on the development of learning-based control policies that facilitate
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Adrian Kent (DAMTP, University of Cambridge), Professor Erika Andersson (Heriot-Watt University), Professor Alex May (Perimeter Institute), Professor Debbie Leung (IQC Waterloo and Perimeter Institute) and
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, University of Cambridge, Downing Place, Cambridge, UK . The appointed Postdoctoral Research Fellow is expected to visit and work together with the other three PIs and their team members – as well as a large