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fully understand how these interventions control water flow, meaning their flood protection benefits may be miscalculated. This PhD will generate new knowledge to support the effective design 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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(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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designed to meet multiple needs in marine biodiversity monitoring. The project aims to develop embedded novel deep learning and computer vision algorithms to extend the system’s capabilities to classify
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Fleshy-fruited Myrtaceae, comprising nearly half of the ~6,000 species, represent a key evolutionary innovation linked to diversification and dispersal. This project investigates the ecological and
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; elsewhere, exposure sustains disinvestment. This PhD investigates these dynamics, known as hazard gentrification, and asks how cities can design adaptation that is resilient and socially just. You will build
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an advantage. English language requirements: Applicants must meet the minimum English language requirements. Further details are available on the International website (http://www.lboro.ac.uk/international
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Flooding is increasing under climate change, with rainfall intensifying risks across climate zones. In the UK, flood defences are traditionally designed as static structures, overlooking the fact
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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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(computer vision technologies). The interdisciplinary nature of this PhD will require the integration of environmental science, engineering, and community science methodologies. Supervisors: Primary