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proposing an ambitious programme of impactful research, teaching and training with geographical foci in Africa, India and the Arctic Nations. This studentship will make an important contribution
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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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-landslides, orographic rainfall effects and extremes), using the volcanic island of Tenerife as a case study. Some work has been done (e.g. on Hawaii), but knickpoint geometry and using state-of-the-art
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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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. Analysis of images will investigate the efficacy of manual digital approaches (e.g., Dot Dot Goose) and the development of a marine litter characterisation and quantification algorithm for automated analysis
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outlining how you would approach the project, and an up-to-date CV. Under programme name, please select 'Architecture, Building and Civil Engineering'. Please quote reference RAINDROP-CH. Only applicants with
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supervision from a leading expert in Rolls-Royce throughout the project. How to Apply: All applications should be made online . Under Campus, please select ‘Loughborough’ and select Programme ‘Mechanical and
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two-page research proposal based on the project description outlining how you would approach the project and what methods you would use. Under programme name, please select 'Architecture, Building and
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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