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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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, 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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, please select ‘Loughborough’ and select Programme ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number * CSC-26-WS * in your application under the ‘Finance’ section
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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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international tuition fees provided by the University. How to Apply: All applications should be made online via the above 'Apply' button. Under programme name, select ‘School of Architecture, Building and Civil
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University. How to Apply: All applications should be made online via the above ‘Apply’ button. Under programme name, select ‘School of Social Sciences and Humanities’. Please quote the advertised reference
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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