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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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objects, by embedding them into a 2 or 3-dimensional space through a representation learning algorithm, has been widely used for data exploratory analysis. It is particularly popular in areas such as
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conductive material. A magnetic induction spectrum is sensitive to the location and geometry of conductive material around a magnetic field source and receiver. Hybrid tomography involves the fusion of two
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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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-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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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
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-treatment facilities, and biorefineries. Feedstock choice, regional dynamics, and process side-streams all affect costs, energy use, and emissions. This PhD project will develop advanced computational models
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Deadline: 12.12.25 How to apply: https://uom.link/pgr-apply-2425 For UK students This 3.5-year PhD studentship is open to Home (UK) applicants and EU students with settled status. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject...
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Application deadline: All year round Research theme: Nanomaterials This 4-year PhD project is fully funded for home students. The successful candidate will receive an annual tax-free stipend based on the UKVI stipend (£20,780 for 2025/26) and tuition fees will be paid. We expect the stipend to...
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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