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Field
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approaches, including principal component analysis and machine learning, to handle multivariate datasets. Prior experience in data science is not essential; the successful candidate will be supported through
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Discipline: Engineering & Technology, Materials Science, Mechanical Engineering Qualification: Doctor of Philosophy in Engineering (PhD) This collaborative project between Jaguar Land Rover (JLR
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the properties of post-consumer recyclates. The project will apply cutting-edge data science techniques, including principal component analysis and machine learning, to handle multivariate datasets and enhance
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Matlab, Python and C/C++ are preferred. Strong interpersonal skills and the capacity to work and learn independently will be required. English Language IELTS 6.0 Overall (with no individual component below
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MATLAB/Python/R etc Previous research experience with healthcare datasets or electronic health records English Language IELTS 6.5 Overall (with no individual component below 6.5) or Swansea University
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donated to research. Our recent data demonstrated successful delivery of homodimeric minimal Factor H during the cold flush period of the organ transplant pathway, which protected human organs for damage
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catalysts. This EPSRC-funded project leverages cutting-edge X-ray spectroscopy to unlock how alkali elements can be harnessed to create powerful heterogeneous catalysts for CO₂ and hydrogen technologies. By
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programming experience (preferably in Python). If English is not your first language, provide evidence of proficiency through: A minimum IELTS average score of 6.5 and a minimum of 6.0 in each component. OR A
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paid. We expect the stipend to increase each year. Only Home students are eligible for funding. The start date is October 2026. The project aims to develop and optimize metal oxide aerogel materials
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elements like Physical Unclonable Functions (PUFs) and True Random Number Generators (TRNGs) to secure hardware components. Embedded Trust Protocols: Design protocols that establish and maintain trust within