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://doi.org/10.1039/D2CC00532H ) that have potential applications in sensing, separations and catalysis. Our research focusses on three distinct challenges to achieving efficient material prediction: i
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of the bioremediation process and offers the potential to provide the underpinning data to metabolically engineer the organism in the future. This ‘omics approach will also enable the prediction of other pollutants
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science interlink prevention and prediction of wildfire risk, by contributing to the development of a fundamental physical model to understand the process of fire spread for wildfires, as part of a European
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. Your work will feed directly into the development of predictive models that link microstructure to performance, guiding the design of alloys that are stronger, more reliable, and more efficient. By doing
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within fusion reactors, especially plasma-facing materials (PFMs) exposed to intense heat fluxes and energetic particles. Understanding and predicting how these materials degrade under such conditions is
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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marginal structural models will be extended with machine learning techniques for counterfactual prediction and to support sensitivity analyses Candidate The studentship is suited to a candidate with a strong
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-aware predictions. The successful candidate will join an international, interdisciplinary team and contribute to AI solutions with direct impact on biodiversity monitoring, conservation planning, and
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into quantitative frameworks for prediction of the contribution of An. stephensi to malaria transmission, and optimising surveillance and control for this and other native vector species in urban settings. 2. Build
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compatibility with traditional composite matrices. Explore complementary computational fluid dynamics-discrete element method (CFD-DEM) simulations as a tool to predict fibre-fluid interactions and inform