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develop AI- and deep learning–based computer vision tools to automatically identify and quantify intertidal organisms. Beyond computer vision, it will leverage machine learning for large-scale, data-driven
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by developing AI methods that improve recognition of rare species while providing reliable measures of uncertainty. Using state-of-the-art computer vision approaches — vision transformers, self
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This PhD project focuses on advancing computer vision and edge-AI technology for real-time marine monitoring. In collaboration with CEFAS (the Centre for Environment, Fisheries, and Aquaculture
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to the interests of one of the School’s research groups: Cyber-physical Health and Assistive Robotics Technologies Computational Optimisation and Learning Lab Computer Vision Lab Cyber Security Functional
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to quickly quantify the damage to forest plantations after a cyclone or a tropical storm. There is unrealised potential in using multi-modal computer vision methods that synthesis multi-source Earth
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the central challenge hindering this vision: the fundamental incompatibility between text-native LLMs and the operational reality of computer networks. Directly applying LLMs is impeded by three core technical
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About the Project Project details: The rapid evolution of battery technologies drives global lithium demand, and the UK’s 2030 vision seeks a competitive, sustainable battery supply chain to fuel
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Peter Hamilton, and Professor Ray Yep), and who also share a vision for public engagement of the discipline. The studentship is funded by an endowed fund at the University. The Centre encourages and
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limited to Professor Robert Bickers, Dr. Vivian Kong, Dr Peter Hamilton, and Professor Ray Yep), and who also share a vision for public engagement of the discipline. The studentship is co-funded by a
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within the Institute of Health Informatics. The studentships will commence from Feb 2026. About the project Successful applicants will work with the programme coordinator to identify a project and a