606 professor-computer "https:" "https:" "https:" "https:" "https:" "Keele University" PhD positions in United Kingdom
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, specialises in laser-matter interactions of metal additive manufacturing, and the co-supervisor, Professor Paul Mativenga (UoM), has extensive expertise in laser materials processing. Dr Chu Lun Alex Leung
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- See advert for details Deadline: 13 April 2026 Supervisors: Dr Zhenhui Yuan and Professor Joseph Hardwicke Predictive Network Intelligence for Medical Telerobotics Research area and project description
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motivated and ambitious PhD candidate skilled in genetic epidemiology, biostatistics, mathematics computational biology or related discipline to contribute to the “Hereditary & Early onset breast cancer
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autonomous systems. Supervision The project will be supervised by Dr Anthony Siming Chen (EEE), with co-supervision from Professor David Branson III (M3) and Professor Praminda Caleb-Solly (Computer
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applied knowledge of qualitative research methodologies, including qualitative interviewing, ethnography, and document analysis. However, when joining the KBS PhD programme the student will receive support
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Supervisors: Dr Negar Gilani and Professor Richard Hague The Centre for Additive Manufacturing (CfAM) Research Group within the Faculty of Engineering at the University of Nottingham, recognised as
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Professor Dirk vom Lehn and Dr Juan Baeza (King’s Business School, PSMO) as well as by Michael Bowen (College of Optometrists). Collaboration The studentship is supported by the Research and Knowledge
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sponsors to deliver the outputs and will have access to a bespoke training programme. Per- and polyfluoroalkyl substances (PFAS), also known as “Forever Chemicals”, are micropollutants of increasing concern
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status rate at the School of Mechanical, Aerospace and Civil Engineering at the University of Sheffield under the supervision of Professor Pierre Ricco (www.pierre-ricco.co.uk ). Additionally, the student
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell