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achieving Net Zero by 2050. In partnership with Plant Health at Defra (Department for Environment, Food & Rural Affairs), this project introduces a novel AI-driven framework to protect the nation’s plant life
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AI techniques for damage analysis in advanced composite materials due to high velocity impacts - PhD
for automated, data-driven diagnostics, integrating AI with high-resolution imaging and sensing offers a transformative solution. AI models can learn to recognize subtle damage patterns, enabling faster, more
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PhD studentship: AI-Driven Fall Detection from Floor Vibrations Award Summary 100% fees covered, and a minimum tax-free annual living allowance of £20,780 (2025/26 UKRI rate). Additional project
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experimental chemistry. Additional training in a wide range of soft and hard skills is available at the University. What you will do: This PhD project aims to bring together multi-scale modelling and data-driven
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to the design of aircraft, wind turbines and medical devices, and for modelling the environment. Remarkable advances in computing driven by the exponential miniaturisation of transistors (Moore’s law) have
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-driven changes in rainfall. This poses significant flood risk to homes, businesses and infrastructure. For example, during the winter of 2015/2016, mine water flooding at Kibblesworth in Gateshead flooded
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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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Software Development: Building the Next-Generation Trust Maturity Model Integrating DevOps practices into ML-driven systems: A Framework and Maturity Model for Continuous Machine Learning Development
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models such as Random Forest and Neural Networks to help understand and predict pairwise interactions between pollinators and plant species. - Software Engineering: integrate models into a standalone
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This PhD project aims to achieve a breakthrough in thermal efficiency (>50%) for dual-piston Free-Piston Engine Generators (FPEGs). The research will pioneer a novel, integrated methodology