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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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(CHF) phenomena – the prediction of which is key to safely designing and operating water based nuclear reactors. Current industrial modelling tools necessitate excessively conservative safety margins
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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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limited time window) is essentially unexplored. Using state-of-the-art climate modelling (e.g. EUROCORDEX) and techniques to identity triplets offers the opportunity of dramatic new insights into extra
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United Nations Sustainable Development Goal 15 aims to protect terrestrial ecosystems, manage forests sustainably, combat desertification, and halt biodiversity loss. Achieving this requires a deeper understanding of how environmental changes affect biodiversity, particularly through accurate...
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language model (LLM) technologies to create advanced, multimodal predictive tools for plant health monitoring. Using imagery from RGB cameras, drones, satellites, and multispectral and hyperspectral sensors
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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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, 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
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create a working framework that includes both experimental and modelling prototypes, including AI/ML tools to assist with the large number of variables involved. This project is seeking candidates with a