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About the project: Developing a Theory of the Magnetic Fingerprint of Stress in Materials Supervisor: Dr Chris Patrick, University of Warwick In the development of sustainable materials and
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. Together, these mechanisms strongly influence the long-term behaviour and durability of these engineering plastics. This PhD project aims to investigate the mechanisms governing the long-term evolution
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substantial hurdles for storage, transmissibility, and long-term curation. This PhD project aims to address these challenges by researching and developing specialized lossless and lossy compression methods
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. This PhD project aims to create advanced XCT workflows by developing Artificial Intelligence (AI) and Machine Learning (ML) tools to support imaging before the reconstruction phase. The research will focus
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understanding of the hot workability and oxidation behaviour of ruthenium alloys is a major barrier to their commercial product development. This aim of this project is to gain a fundamental understanding of the
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develop next-generation power converters for mission-critical applications: from hyperscale AI hubs to MW-scale EV charging and smart grids. The UK’s Modern Industrial Strategy and commitment to Net Zero
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Funding for: UK Students Research area and project description: Develop scalable acoustic methods to structure advanced polymer composites for lightweight, low‑carbon technologies. This PhD explores
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early detection and predict adverse pregnancy outcomes. You will develop and validate a data-driven clinical decision support tool in collaboration with clinicians and industry partners. Pre-eclampsia is
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reliable transmission of demanding multi-modal data such as haptic feedback, video, and 3D sensing data. This project will develop AI-driven predictive network intelligence to anticipate delay and network
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(such as demand spikes) can threaten the power grid stability. The PhD project will identify and develop solutions to mitigate power grid instability caused by AI data center loads, ensuring resilient grid