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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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(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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lithology, climate, catchment size), with sediment and laser scanning data offering a secure and rapid start to the work. This PhD has an international supervisory team providing expertise in volcanic island
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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
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minimum English language requirements. Further details are available on the International website . Funding information: This PhD project is jointly funded by EPSRC (via the Industrial Doctoral Landscape
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should be made online . Under programme name, select School of Architecture, Building and Civil Engineering. Please quote the advert reference FCDT-26-LU9 in your application. This PhD is being advertised
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) offering a secure start to the work. This PhD will be an inter-disciplinary, using expertise on meteorology and co-occurring hydro-meteorological hazards (Chen, Hillier, Bloomfield) and insurance industry
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the above 'Apply' button. Under programme name, select ‘School of Social Sciences and Humanities’. Please quote the advertised reference number, ‘FCDT-26-LU2’, in your application. This PhD is being
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University. How to Apply: All applications should be made via the 'Apply' button above. Under programme name, select Department of Geography and Environment. Please quote the advertised reference number: FCDT