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strategic priorities in Digital Twins, Environmental Intelligence, and Data-Driven Engineering, using advanced computational modelling to support ecosystem resilience and sustainable management. The project’s
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the entry requirements for the potential programme you are applying for. To Apply for this project please click on the following link -https://www.exeter.ac.uk/study/funding/award/?id=5734 Funding details
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) Supervisors Dr Chris Hackney Eligibility Criteria You must have, or expect to gain, a minimum 2:1 Honours degree or international equivalent in a subject relevant to the proposed PhD project (inc. computing
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analysis, critical thinking, scientific writing Your supervisors will help you identify your training and development needs, and use dedicated time and funding within the ARIES DT programme to support your
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well as academic guidance and mentorship. The scholarship may be awarded to applicants who have just completed their undergraduate studies; or have completed a Bachelor’s degree or a Master’s programme and/or have
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opportunities. Be mentored by internationally recognised research leaders Our cohort-based, 4-year scheme offers a distinctive PhD training experience. During this structured PhD programme, you will complete
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-represented groups in physics and astronomy including, but not limited to, women and Black, Asian and Minority Ethnic. The research programme will take place in the an interdisciplinary environment
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importance to equal opportunities, the advancement of women and diversity . We lay special emphasis on increasing the number of women in senior and in academic positions among the academic and general
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, and edge computing, AASs are evolving into smart, interconnected solutions for addressing the dynamic challenges of modern cities. This project investigates how to coordinate these multifunctional
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
intelligence, particularly in computer vision and deep learning, offer an opportunity to automate and enhance damage assessment by learning patterns from multimodal data. This research seeks to bridge the gap