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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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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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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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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
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learning and experience in two or more of: computer vision, sensors/sensor fusion, robotics fundamentals. • Proficient in programming languages such as Python and C++; experience with frameworks such as
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of short-axis MR image sequences. Training You will be based at the Vision Computing Lab within the School of Computing Sciences, which specializes in deep learning for medical image analysis and neural
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, FIFA, England & Wales Cricket Board, New Balance, Nike, UK Sport, Reebok, Speedo, and others, in the design, simulation, testing, and manufacturing of sporting goods. Our vision is to provide world
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, National Trust, John Muir Trust and Warwickshire Wildlife Trust aims to address this. As part of the Heritage Lottery Fund’s Nature in Towns and Cities Programme (NiTC) Green4all’s vision is to transform
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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary