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scattering with computer modelling such as molecular dynamics simulations and AI-assisted data mining. The new technical capabilities will help bridge the current gap in biocide development, i.e., to link
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. The project will investigate how advanced and modern cryptographic protocols, such as zero-knowledge proofs, secure multiparty computation, homomorphic encryption, exotic signatures, and their post-quantum
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to enhance the post-THA patient experience. Applicants should have, or expect to achieve, at least a 2.1 honours degree or a master’s (or international equivalent) in a relevant science or engineering related
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Engineering, Computer Science or related disciplines. Experience in autonomous system, manufacturing/robotics and machine vision development will be an advantage. To apply please contact the supervisor, Dr Kun
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approaches, assess mechanical and filtration performance post-regeneration, and quantify environmental trade-offs using ISO 14040/44-compliant lifecycle assessment frameworks. Guided by the principles
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Research theme: "Next Generation Wireless Networks", "Signal Processing", "Machine Learning" UK only How to apply: uom.link/pgr-apply-2425 This PhD project aims to design novel resource allocation and signal processing methods using machine learning techniques to enhance the resilience,...
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-free stipend based on the UKVI amount (£20,780 for 2025-26). We expect the stipend to increase each year. This studentship is related to a multi-institutional EPSRC Programme Grant “AMFaces: Advanced
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to increase each year. Tuition fees will also be paid. Home students are eligible. A funded PhD studentship is available in the field of computational inorganic chemistry. The project will involve prediction
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Deadline: All year round How to apply: https://uom.link/pgr-apply-2425 UK only This 3.5-year PhD project is open to home students; the successful candidate will receive an annual tax free stipend based on the UKVI amount (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend...
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Gamesa Renewable Energy R&D team and also undertake an industry placement as part of the PhD programme. To apply, please contact the main supervisor, Dr Chen - lujia.chen@manchester.ac.uk . Please include