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engineering, clinical research, and AI-driven health monitoring. This project will explore large-scale maternal datasets—combining clinical cardiovascular assessments with wearable sensor data—to detect early
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, dynamical systems and statistical physics. The candidate will be jointly supervised by the Coventry team Dr Fei He and the Stellenbosch team Prof. Francesco Petruccione . This project will contribute
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Supervisory Team: Prof Middleton, Prof Gandhi PhD Supervisor: Matt Middleton Project description: We know of only 20 or so black holes in our galaxy yet predict there should be 10s of millions
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—remains a critical challenge. This project will focus on designing AI-driven cognitive navigation solutions that can adaptively fuse multiple sensor sources under uncertainty, enabling safe and efficient
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related discipline. To apply, please contact the supervisor, Prof Foster - david.foster@manchester.ac.uk . Please include details of your current level of study, academic background and any relevant
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of this project, please contact Prof. Tony McNally (T.McNally@warwick.ac.uk ) directly for further information. Essential and Desirable Criteria: 1 or 2.1 degree in Materials Science & Engineering, Chemical
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to the Centre. Warwick Manufacturing Group Three successful offer holders will be under the supervision of Prof. Louis Piper and Dr. Mel Loveridge in the Warwick Manufacturing Group. Prof Piper and Dr Loveridge
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which there exists extensive experience in the areas of machine learning, biostatistics, and medicine: Dr Yanda Meng and Dr Tianjin Huang (Machine Learning), Prof Yalin Zheng (AI in Healthcare), A/Prof
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the ranking. However, STV method becomes considerably more complex with encrypted ballots. Our goal is to develop an algorithm/protocol to count encrypted ballot using the STV method. Our first point of
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, digital twin enabled process development and life cycle assessment will be researched. Primary Supervisor: Prof Xichun Luo Additional Supervisor/s: Dr Matt Boyle Contact Details: For further details please