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be possible, please contact Professor Chris Proudman once deadline passes. You will need to meet the minimum entry requirements for our PhD programme . Additional requirements: Familiarity with horses
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This 4-year PhD programme is fully funded for home students; the successful candidates will receive a tax free stipend based on the UKVI rate (£20,780 for 2025/26) and tuition fees will be paid
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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Partnership at the University of Cambridge. This programme is a collaboration between industry and academia, now in the final 24 months of a 60-month effort. The programme integrates research and innovation
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field. A background in organic chemistry, and/or high-throughput experimentation is desirable. Proficiency in programming languages (Python/MATLAB) commonly used in machine learning applications is
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to apply, please use the below Programme Application Portal to get in touch with the Programme Admission Admin. Application received in other channels will be re-directed and requires apply for a second time
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Kevin Wilson, School of Mathematics, Statistics & Physics Dr Holly Fisher, Population Health Sciences Institute Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree
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Applications are invited for a Senior Researcher in Decision Analytic Modelling, working with Associate Professors Apostolos Tsiachristas and Brian Nicholson, on a research programme developing and
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requirements can be viewed on the relevant programme page here . Nationality restrictions This funding is available to all nationalities. Application procedure Apply for a place on the PhD programme of
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
this, you will work with emerging AI techniques such as large language models, which can interpret technical documentation, and knowledge graphs, which help structure and connect engineering knowledge