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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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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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will yield spatially varying uplift information on timescales of ~1-10 ka, complementing the historical or archaeological ( 0.1 Ma) records. Pilot fieldwork has identified key sites (e.g. varying
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strong social economy. Building on reflexive ethnographic methodology, data will be analysed collaboratively with the research participants. During fieldwork the researcher will undertake ethnographic
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, primarily due to validation gaps in high-pressure boiling phenomena and the lack of high-fidelity experimental data. Building on recent advances in non-intrusive, high-resolution optical diagnostics
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or further information, please direct all queries to Dr. Lulu Xu at l.xu@lboro.ac.uk Funding information: As this is a China Scholarship Council (CSC) based scholarship this is only open to Chinese nationals
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respond to intense rainfall and enhanced sediment transport. The successful candidate will develop advanced skills in geomorphology, hydrology, environmental data analysis, and climate resilience
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, primarily due to validation gaps in high-pressure boiling phenomena and the lack of high-fidelity experimental data. Building on recent advances in non-intrusive, high-resolution optical diagnostics
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minimum English language requirements. Further details are available on the International website . Funding information: Studentship type – UKRI through FLOOD-CDT The studentship is for 3.5 years and
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. Funding information: Studentship type – UKRI through FLOOD-CDT . The studentship is for 3.5 years and provides a tax-free stipend of £20,780 per annum plus tuition fees at the UK rate. Due to UKRI funding