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processing and storage, and supporting compliance with stringent certification requirements. Cranfield University offers a distinctive research environment renowned for its world-class programmes, cutting-edge
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spaces and habits for them. This is a highly interdisciplinary project that combines computational modelling and behavioural science. The first part will be based on the use of state-of-the-art
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Applications are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in manufacturing engineering. The successful candidate will be based
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are invited to undertake a three-year PhD programme in partnership with industry to address key challenges in on-platform manufacturing engineering. The successful candidate will be based at the Rolls-Royce
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computational and machine learning approaches to integrate Oxford Nanopore (ONT) long-read data with bulk and single-cell RNA-seq profiles. The aim is to identify host-microbiome molecular signatures that drive
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applicant must have (or be close to obtaining) a relevant PhD in Fluid Mechanics from an Engineering, Mathematics or Physics Department, a strong background in theoretical and computational fluid mechanics
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This 3.5-year PhD studentship is open to Home (UK) applicants and EU students with settled status. The successful candidate will receive an annual tax-free stipend set at the UKRI rate (£20,780 for 2025/26; subject to annual uplift) plus home tuition fees will be paid. We recommend that you...
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methodological approach through three key objectives, focusing on generations, intergenerational practice and policymaking and programmes in community sport for development. respectively. A fourth objective
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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relevance. A digital twin framework for safe, simulation-based validation before deployment in operational wind farms. Develop explainable AI (XAI) frameworks and human-computer interfaces that enable wind