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address Dr. Chunwei Xia: c.xia@leeds.ac.uk Co-supervisor’s full name & email address Professor Zheng Wang: z.wang5@leeds.ac.uk Project summary Large Language Models (LLMs) have profoundly transformed
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Research theme: Materials 4.0 No. of positions: 1 Open to: UK applicants This 3.5-year project is funded by the School of Natural Sciences and is available for home students; the successful candidate will receive an annual tax free stipend set at the UKRI rate (£20,780 for 2025/26). Tuition fees...
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Trust. The successful candidate will work closely with the PI and a PhD student within a larger cross-disciplinary team to construct a quantitative computational model of carbonate biomineralisation
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As part of the Restoration Ecology and Dynamics (READY) Doctoral Focal Award, we invite applications to the following PhD project: Measurement and modelling of future persistence of restored
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and prosthetic devices in the real-world. This PhD project offers the opportunity to work on pioneering research that combines state of the art computational modelling (deep neural networks) and
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; Midlands Graduate School Doctoral Training Partnership | Loughborough, England | United Kingdom | 2 months ago
Analysis of Individual Heterogeneity and Discriminatory Accuracy (MAIHDA) approach was recently developed to model such intersectional inequalities. This PhD will involve developing and applying the MAIHDA
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desorption mechanisms of various FFA on different type of metallic surfaces as a function of temperature and concentration. The modelling data and principal component analysis will be used to build property
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promise in understanding disease mechanisms and improving clinical decision-making. Recent studies suggest that generative models can uncover latent structures and improve classifier robustness across
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within the energetics field. Whilst many reactive burn models have been developed to simulate this behaviour, they are generally designed for ideal explosives and are mostly engineering models fitted
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model due to the mathematical challenge of solving the multiple partial differential equations simultaneously. With the support of the combined sponsorship from the university and industrial partner