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assurance/insurance frameworks, including algorithmic insurance contracts, parametric insurance structures, and data-driven risk quantification methods (e.g., conformal prediction and inverse conformal
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meaningful contribution and thrive within a School committed to growth and excellence. This full-time post is available immediately on an indefinite basis. Further information on our great benefits package
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the Applicant Information Pack, available to download from the RCM website: www.rcm.ac.uk/jobs To apply, please complete our: Application form, and Equal Opportunities form (available to download from the RCM
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secondment, subject to agreement with your current line manager. Additional information Contract type: Fixed Term Contract – Maternity cover until June 2027 Work pattern: 21 hours per week/ 0.6 FTE Shift
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throughout the academic year. This role will suit someone who is confident working with data, enjoys coordinating complex processes, and takes pride in delivering accurate, well-organised support. Key
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reliable transmission of demanding multi-modal data such as haptic feedback, video, and 3D sensing data. This project will develop AI-driven predictive network intelligence to anticipate delay and network
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demonstrable molecular biology skills. You will be able to design the experiments, execute the experiment using various molecular biology techniques, and help analyse the data. You will have highly effective
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. Richard Nickl and work in the project area 'Statistical aspects of non-linear inverse problems', which broadly addresses mathematical and foundational challenges in contemporary data science and the theory
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. The hours of work are variable (up to 25 hours per week, 5 out of 7 days per week) and will include weekend work. Hourly rate is £15.07, which includes holiday provision. For further information, please
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endemic countries. We are seeking to appoint a Research Fellow to join a research programme that applies advanced bioinformatic, statistical, and population genomic approaches to large-scale sequencing data