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proposal. This PhD will evaluate the efficacy and suitability of digital image collection and analysis for beach litter characterisation on heavily-littered coastlines, in partnership with community groups
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databases. Integrating image- and text-derived datasets poses challenges due to differences in scale, structure, and accuracy, requiring robust data fusion and validation. By combining these AI-derived trait
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-driven shifts in species distributions. Currently, barnacles and other species are manually counted from over 3,000 images each year, which is time-consuming and prone to human error. This project will
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, and epibenthic biodiversity. The project will build on a working prototype, the Neural Network Enhanced Marine Observation system, a low-cost, shallow-water, edge-AI-enabled spatial camera system
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, and community resilience across vulnerable deltas. We welcome applicants with quantitative aptitude and curiosity about rivers, hazards, and sustainability (training provided in GIS, coding, and