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Applications are invited for a PhD project within the Faculty of Engineering, in the Centre for Additive Manufacturing research group (CfAM) at the University of Nottingham. The student will work in
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immune regulation, inflammation, or disease susceptibility. This project offers a unique opportunity to work at the intersection of human genomics, microbiome science, and computational biology, with
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deadline passes. You will need to meet the minimum entry requirements for our PhD programme . Candidates should have a strong background and first degree in chemistry or closely related subject (BSc/MChem
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based sensors to deliver resilient, high-accuracy positioning. The project sits at the intersection of...
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Francesco Fanicchia is a recognised expert in advanced surface engineering and the development of multifunctional protective coatings, specialising in thermal barriers and fire-resistant materials. As a
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technology, management, defence and security. Cranfield is recognised for delivering outstanding research addressing contemporary global challenges with economic, environmental, and social impact for business
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Funding: School of Computer Science Studentship consisting of the award of fees, together with a tax-free maintenance grant of £20,780 per year for 3.5 years. Lead Supervisor’s full name & email
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-assurance positioning in safety-critical applications. Cranfield is a specialist postgraduate university that is a global leader for education and transformational research in technology, management, defence
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, process stability, and the downstream consolidation and performance of remanufactured composites. This fully-funded PhD project fits within a wider research programme with industrial partners and an
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, interpretable models from experimental and operational data. The core goal is to balance model accuracy with computational efficiency, while meeting the needs of experimental validation. The framework will