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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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microclimates that demand dense sensor networks and reliable data retrieval. This project focuses on developing nature-inspired hardware to deploy Internet of Things (IoT) sensors in forest ecosystems. Combining
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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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, 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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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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the life cycle of its products. Existing practices often overlook indirect (Scope 3) emissions and fail to integrate real-time data analytics or life cycle assessments for decision-making. Therefore