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, and play an important part in accelerating the progression towards net zero in the UK and globally. In this PhD project, the student will lead on computational methods, including molecular dynamics (MD
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proposing an ambitious programme of impactful research, teaching and training with geographical foci in Africa, India and the Arctic Nations. This studentship will make an important contribution
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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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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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outlining how you would approach the project, and an up-to-date CV. Under programme name, please select 'Architecture, Building and Civil Engineering'. Please quote reference RAINDROP-CH. Only applicants with
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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
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select Programme Ph.D. Sport, Exercise and Health Sciences. Please quote the advertised reference number SSEHS/LJ26 in your application. To avoid delays in processing your application, please ensure
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two-page research proposal based on the project description outlining how you would approach the project and what methods you would use. Under programme name, please select 'Architecture, Building and
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two-page research proposal based on the project description outlining how you would approach the project and what methods you would use. Under programme name, please select 'Architecture, Building and
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, the project accelerates trait data acquisition by applying computer vision to herbarium specimens and field photos, as well as large language models to extract complementary information from literature and