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systems, providing early detection of adverse events such as infection and inflammation. The project will involve sensor design and modelling, prototype development, electrochemical characterisation, and
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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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which will cover tuition fees for UK-based students and provide an annual stipend in line with EPSRC recommended levels (£20,780 for 2025/26), with an expected start date of 1st January 2026
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will develop and evaluate new approaches to predicting current and future population exposure to such hazards by combining numerical modelling and remote sensing of river migration, with machine learning
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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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to align with their background and interests. Example topics include: Groupoid models for C*-algebras; Cartan subalgebras of C*-algebras; Semigroup C*-algebras; Ring C*-algebras; C*-algebras from algebraic
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considerations in topological dynamics, algebra, or group theory. The research problem will be co-created with the student to align with their background and interests. Example topics include: Groupoid models
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resilience. With only a small fraction of soil microbial functions currently understood, there is a clear need to design evidence-based, functionally coherent microbial consortia to support sustainable
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factors, including geographic location, hydrodynamic conditions, and water depth. This PhD will build upon numerical modelling studies to employ physical modelling experiments in state-of-the-art facilities
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the model liverwort Marchantia polymorpha. In particular, we focus on interactions with fungus-like filamentous oomycete phytopathogens belonging to the genus Phytophthora, which provide a unique platform to