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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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Join an exciting research journey at the intersection of advanced materials, electronics, and textile engineering. This PhD project will explore smart structural design, manufacturing and
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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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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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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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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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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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fully understand how these interventions control water flow, meaning their flood protection benefits may be miscalculated. This PhD will generate new knowledge to support the effective design and