9 parallel-and-distributed-computing "Multiple" PhD positions at Loughborough University
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as early indicators of anthropogenic and climate-driven change. However, limited understanding of the processes shaping species’ biogeographic distributions constrains our ability to predict ecological
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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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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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. road, power distribution). This project will develop new insights into what drives flooding and extreme wind to co-occur on timescales from (sub-)daily to seasonal. Our recent work suggests that
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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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Science, Computer Science, Engineering, or an appropriate Master’s degree. English language requirements: Applicants must meet the minimum English language requirements. Further details are available
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‘Campus’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number ‘FP-HZ-2025’ in your application under
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’, please select ‘Loughborough’ and select the ‘Programme’ as ‘Mechanical and Manufacturing Engineering’. Please quote the advertised reference number of ‘FP-CTS/2-2025’ in your application. Applications must
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would approach the project and what methods you would use. Under Campus, please select ‘Loughborough’ and select programme ‘Architecture, Building and Civil Engineering’. Please quote the advertised