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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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from the UK Health Security Agency and real-world outbreaks. This interdisciplinary project integrates microbiology, molecular biology, bioinformatics, and public health, offering hands-on training and
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replacement, statistics and working with joint registry data. You will be based in the Integrative Musculoskeletal Biomechanics (IMSB) research group and join a thriving PhD research community in the School
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integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in organic/polymer synthesis and/or material analysis will also be beneficial. EU
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integrated Masters, a BSc + MSc or a BSc with substantial research experience will be highly advantageous. Experience in organic/polymer synthesis and/or material analysis will also be beneficial. EU
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the world, the PDS candidate appointed into this role will have the opportunity to work at the interface of research, industry and policy. You will become an integrated member of the Task, acting as a bridge
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of advanced computational techniques. This research will integrate power system modelling, optimisation algorithms, and artificial intelligence (AI) techniques to develop an innovative framework for strategic
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hydrological outcomes which may feed back to the cryospheric system. So far ecological, hydrological, and cryospheric work has been conducted separately. To address this gap, our project integrates
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This project aims to bridge the gap between technological advancements and their integration into societal and environmental systems by shifting from a product-centric to a service-oriented approach
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reconciliation, enhancement, and integration of the MLGB dataset, working with legacy print, manuscript, and digital sources. You will apply and adapt digital methods (especially TEI XML), analyse provenance data