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-convex optimisation, performance analysis, machine learning, etc.) 1) Applicants should have, or expect to achieve, at least a master’s (or international equivalent) in a relevant science or engineering
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to determine both AGN activity and star formation directly from survey imaging data, bypassing traditional catalogue-based analyses. The project is funded by the Science and Technology Facilities Council (STFC
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motivated students with a strong background in engineering or computer science. The ideal candidate will have: Strong programming and software skills. An awareness of machine learning theory and techniques
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or equivalent in a subject relevant to the proposed PhD project, including chemistry, materials science, engineering, physics or a related field. The studentship covers fees at the Home rate (UK and EU applicants
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Hours of work: Part-time / 0.5 FTE Tenure: Fixed term for 12 months This is an exciting opportunity to contribute to a collaborative Engineering and Physical Sciences Research Council (EPSRC)-funded
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, working alongside leading academics, researchers, project engineers and fellow PhD candidates. The group takes a holistic approach to metallic materials, examining the fundamentals and how they influence
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study the microstructure of nickel-based alloys used in turbine wheels — vital components for hydrogen-ready engines and future power technologies. Your work will feed directly into the development
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growing threat to aquatic environments, while increasingly stringent phosphorus discharge limits are placing pressure on wastewater management systems. The project will investigate the use of engineered
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is an innovative, interdisciplinary fully funded PhD programme that brings together science, engineering, and mathematics to tackle some of the most pressing challenges of our time. Big Questions, Real
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, engineering, and mathematics to tackle some of the most pressing challenges of our time. Big Questions, Real Impact – From climate modelling and sustainable energy to advanced materials and biomedical systems