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/emissions modelling preferred but not required Creative problem-solving skills and ability to work independently *Candidates with a PhD in other disciplines may be eligible if they can demonstrate exceptional
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to collaborate not only with peers in Swansea but also with partners across the UK fostering a rich and diverse research environment. Your role will be to develop robust remanufacturing process
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to modulate pathological cell behaviour in vitro, with potential for progression to in vivo testing in a rodent model of glaucoma. Candidate Profile Applicants should hold (or expect to obtain) a 1st or 2:1
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may apply stricter criteria to certain nationalities that may be over-represented in Singapore. As part of the offer/nomination process, The University of Manchester will select the most outstanding
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testing and computational modelling. You'll become part of a diverse, multidisciplinary team that prioritises equity, diversity, and inclusion, gaining specialist expertise in hydrogen-material interactions
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emissions and are being implemented to provide a carbon efficiency assessment of farm enterprises. The contribution and management of ensiling at the farm scale to these models has not been fully explored
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace – In Partnership with Rolls-Royce PhD
placement with Rolls-Royce. The research focuses on AI-driven digital twins, using large language models and knowledge graphs for predictive maintenance in aerospace systems. Aerospace systems generate vast
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models. This theoretical project will facilitate close collaboration with experimental groups and enable benchmarking of theoretical predictions. The PhD researcher will be part of the Correlated Quantum
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for household who stay indoors, and to prepare for emergency responses. Possible quantitative methodologies include concurrent time-series analysis of outdoor and indoor environment data, prediction model
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modelling to provide a robust framework for integrating nature-based solutions into SO management. This can alleviate the pressure on treatment infrastructure and reduce dependence on grey infrastructure