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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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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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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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, 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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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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quantitative data, including spreadsheets, emissions databases, or modelling tool possess strong analytical and communication skills, with the ability to engage and collaborate effectively with both academic
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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
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/ ) under the ‘Our project-based studentships’ section on that page All applications should be made via the above ‘Apply’ button. Under Campus, please select ‘Loughborough’ and select the Programme “School