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https://www.sems.qmul.ac.uk/research/studentships/674/net-zero-natural-resource-energy-and-time-efficient-manufacturing/The PhD work will benefit from access to unique facilities including
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This is an exciting PhD opportunity to develop innovative AI and computer vision tools to automate the identification and monitoring of UK pollinators from images and videos. Working at
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The PhD studentship will be based at the University of Cambridge in the Department of Materials Science and Metallurgy as part of the Structural Materials Group. The Structural Materials Group is a
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, and information about the upcoming webinar, please visit: https://www.rca.ac.uk/study/apply-to-study/funding-your-studies/funding-for-mphil-and-phd-students/doctoral-landscape-sch-net-zero-futures/ RCA
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sciences, computer science) PhD close to completion in field of Power Systems, Smart Grids, Power Electronics and Control, or related discipline (upon PhD completion, will transition to Research Associate
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Advances in computing, experiments, and information will continue to reshape engineering in the next decade. This PhD position will nurture a multidisciplinary innovator with the tools to unravel
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This self-funded PhD opportunity explores assured multi-sensor localisation in 6G terrestrial and non-terrestrial networks (TN–NTN), combining GNSS positioning, inertial systems, and vision-based
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achieving Net Zero by 2050. In partnership with Plant Health at Defra (Department for Environment, Food & Rural Affairs), this project introduces a novel AI-driven framework to protect the nation’s plant life
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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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Deadline: 15.10.25 For UK students 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