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enabled services that support education, research, workplace, corporate services and infrastructure enablers to all our staff and students. This large and highly innovative department is undergoing a
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Neuro-Symbolic AI for Trustworthy Clinical Decision-Making: Bridging Linguistic Fluency and Logical Reasoning in Large Language Models (S3.5-COM- Valentino) School of Computer Science PhD Research
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Overview We are looking for a Research Associate to join our team exploring how horizontal gene transfer (HGT) shapes plant evolution. This position will bridge large-scale comparative genomics and
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sustainable chemical innovation. Transferable skills workshops: Master data management, big data methods, research practices, communication, and more. Systems-solutions workshops: Collaborate across disciplines
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, breakthroughs increasingly depend on the ability to process vast amounts of information quickly and efficiently. But as our appetite for computing power grows, so does its cost both in terms of money and
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intersection of systems engineering and healthcare delivery. You will work with a highly interdisciplinary research team and with our clinical and health data partners to ensure that systems engineering
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Neuro-Symbolic AI for Trustworthy Clinical-Decision Making: Bridging Linguistic Fluency and Logical Reasoning in Large Language Models (S3.5-COM- Valentino) School of Computer Science PhD Research
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Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth)
Digital and sensor based conformance validation for large scale forged components (C4-AMR-Crawforth) Advanced Manufacturing Research Centre PhD Research Project Competition Funded Students Worldwide
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enabled services that support education, research, workplace, corporate services and infrastructure enablers to all our staff and students. This large and highly innovative department is undergoing a
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downtimes and very large repair costs. Thus, early and accurate fault detection systems are needed to enable reliable operation and to prevent failures and incurred costs timely. Traditional fault detection