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the question ‘how close to failure is the asset?’. By integrating a suite of state-of-the-art sensors and monitoring technologies with data fusion and AI analytics, this research will enable timely
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on the International website (http://www.lboro.ac.uk/international/applicants/english/ ). Funding information: Studentship type – UKRI through Flood-CDT (https://flood-cdt.ac.uk/ ). The studentship is for 3.5 years and
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minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international/applicants/english/ ). Funding information: Studentship type – UKRI
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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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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary
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relevant subject. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk
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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