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to the full extent. The objectives of this project are i) to quantify the carbon footprint of silage production under different management practices using data collected in farm audits undertaken across the UK
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that facilitate seamless integration between AI hardware components and embedded systems, ensuring efficient data flow and processing. Cranfield University offers a distinctive research environment renowned for its
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methods in the past. A piece of comprehensive computer software, Pythia with the corresponding capabilities have been developed and tested successfully in several industrial applications. The software can
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further information regarding this studentship, please visit the following website: https://www.exeter.ac.uk/study/funding/award/?id=5558
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health management (IVHM) system that leads to enhance safety, reliability, maintainability and readiness. Generally, prognostics models can be broadly categorised into experience-based models, data-driven
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are unable to provide feedback on individual applications, as outcomes are determined by independent panels. Deferral information Offers of funding are not deferrable. If you cannot start your programme by
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us to run large numerical simulations with billions grid points on mixed computer architectures including CPU and GPU machines. A current project is preparing the code set for the next generation of
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powerful framework for decentralised machine learning. FL enables multiple entities to collaboratively train a global machine learning model without sharing their private data, thus enhancing privacy
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, climate sciences or related subject areas. Applications should be made via the above 'Apply' link. Further information can be obtained by emailing Dr Gregor Leckebusch (g.c.leckebusch@bham.ac.uk ). Funding
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: Prof Alex Dickson Further information: The successful applicant will join the Energy and Environment research cluster in the Department of Economics in Strathclyde Business School, and will be part of a