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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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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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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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Rising temperatures are intensifying climate-related risks in cities worldwide, with the greatest impacts often felt by marginalised communities. This PhD project investigates how nature-based
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) offering a secure start to the work. This PhD will be an inter-disciplinary, using expertise on meteorology and co-occurring hydro-meteorological hazards (Chen, Hillier, Bloomfield) and insurance industry
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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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, to support the experimental activity. The candidate will be joining a multidisciplinary team in a lab where we design, make and validate materials and structures. This PhD will expose the successful candidate
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, there is a critical need to develop and implement life cycle carbon accounting tools and optimization techniques tailored to the specific operations and materials used in the pipe industry. This PhD is co