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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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(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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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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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
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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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sediment surges that transform rivers, degrade water quality, and increase flood risk. This PhD will investigate how fire and flood interact to drive these cascading hazards, from hillslopes to rivers
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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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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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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
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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