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Fully Funded PhD Research Studentship tax-free stipend of £20,870 Design, Informatics and Business Fully Funded PhD Research Studentship Project Title: Autonomous AI-powered red agents for enhanced
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benchmarking (Frank et al., arXiv:2024) using blind testing approaches. The student will develop verification-based benchmarking approaches for digital quantum computers with realistic noise models, leading
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), the third leading cause of cancer deaths, often arising from fatty liver disease. While IGRT shows promise in treating HCC, its use is limited by radiation-induced liver fibrosis (RILF). Using a mouse model
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. The student will receive advanced training in quantitative analysis, experimental design, and computational modelling*, and benefit from close collaboration with researchers and policy advocates. The project
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, into simulation models used for health systems management. The research will provide practical and methodological contributions. The framework will offer healthcare decision-makers better tools for designing
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advantage of supercomputing resources at facilities based in Swansea, in the United Kingdom, and overseas. The student will perform the first extended, high precision numerical study of such models, computing
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The project: The deployment of generative AI—particularly Large Language Models (LLMs) based on transformer architectures—in industrial settings poses several critical challenges. Ensuring reliable
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AI-Driven Digital Twin for Predictive Maintenance in Aerospace - In Partnership with Rolls-Royce PhD
intelligent methods that integrate large language models (LLMs) and knowledge graphs to interpret technical documentation and structure complex engineering knowledge. The goal is to create digital twins
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intensive training in energy modelling, AI-accelerated optimisation, and lifecycle-aware computing. Whether working on smart mobility, sensor nodes, or autonomous platforms, you’ll be contributing to a new
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of the Discrete Choice Experiments in Mozambique and South Africa, and the analysis of the ensuing data Develop computational models of health actors’ decisions and responses to regulatory options Collaborate with