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is also desirable. The project will be based at the Institute for the Science of Early Years (www.isey.org ) in London, UK. Supervision will be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with
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Early Years (www.isey.org ) in London, UK. Supervision will be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen
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be by Prof Sam Wass (www.profsamwass.com ) (UEL, UK), with Prof Davidson (UEL, UK), Prof Rachel Barr (Georgetown, US) and Dr Sarah Jessen (Lübeck, Germany) as co-supervisors. The PhDs will start in
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for greater precision. Machine learning (ML) algorithms will analyse these datasets to deliver a scalable, cost-effective system, validated through field trials and enhanced by contributions from four
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). Supervisors Primary supervisor: Prof. G.Tasca. Co-supervisors: Prof. J.Diaz-Manera, Dr. S.Cockell. Eligibility Criteria You must have, or expect to achieve, at least a 2:1 Honours degree or international
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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