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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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system stems from the need to increase efficiency in marine monitoring. Furthermore, existing computer vision solutions often depend on cloud computing infrastructure and require specialized expertise. A
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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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, 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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by detecting and predicting threats such as pests, diseases, and environmental stress in line with the UK Plant Biosecurity Strategy. The project harnesses computer vision, deep learning, and large
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Science, Computer Science, Engineering, or an appropriate Master’s degree. English language requirements: Applicants must meet the minimum English language requirements. Further details are available
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: Applicants should have, or expect to achieve, at least a 2:1 honours degree (or equivalent) in Geography, Environmental Science, Computer Science, or Engineering. A relevant master’s degree and/or experience
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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
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the 'Apply' button above. Under programme name, select School of Architecture, Building and Civil Engineering. Please quote the advert reference FCDT-26-LU8 in your application. This PhD is being advertised as