29 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" PhD positions at The University of Manchester in United Kingdom
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in Experimental Particle Physics (https://www.hep.manchester.ac.uk/study/ ). Our group (https://www.hep.manchester.ac.uk/ ) is one of the largest research groups in the UK with over 100 members
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Application deadline: 30/05/2026 How to apply: https://uom.link/pgr-apply-2425 This 4-year PhD studentship is open to Home (UK) applicants. The successful candidate will receive an annual tax-free
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Application deadline: 31/03/2026 Research theme: Nuclear Materials Hoe to apply: https://uom.link/pgr-apply-2425 UK only This 4-year PhD project is fully funded by the Nuclear Decommissioning
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Application deadline: 30/06/2026 Research theme: Applied Mathematics, Continuum Mechanics, Nonlinear PDEs How to apply: https://uom.link/pgr-apply-2425 UK only due to funding restrictions. The
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guest exchange (J. Am. Chem. Soc. 2025, 147, 17201 https://doi.org/10.1021/jacs.5c02868 ). It is the aim of this project to use this novel methodology to investigate a range of single crystal-to-single
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: machine/deep learning, numerical modelling, statistics, optimisation, scientific computing • Ability to work across disciplines and collaborate with academic and industrial teams Desirable: • Experience in
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-driven AI models that capture the underlying process–structure–property relationships governing metal additive manufacturing. By combining mechanistic modelling, in-situ sensing, and machine learning
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for translational biocatalysis, addressing critical needs in the development of sustainable biotechnologies. The programme will equip PhD students with advanced expertise in enzyme science, machine learning, enzyme
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applications require precision machining to achieve their final geometries. If machining conditions are not kept within specification, then damage to the material can occur, which can be detrimental to fatigue
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they can reliably, affordably, and fairly support a net-zero energy system. The research will focus on how data-driven and machine-learning-based control can coordinate demand, storage, and local generation