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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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, 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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the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select the Programme “Mechanical and Manufacturing Engineering”. Applications must include a CENTA studentship allocation form , a
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‘Campus’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number ‘FP-HZ-2025’ in your application under
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’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number of ‘FP-CTS/2-2025’ in your application. Applications must
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would approach the project and what methods you would use. Under Campus, please select ‘Loughborough’ and select programme ‘Architecture, Building and Civil Engineering’. Please quote the advertised