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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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systems remain too complex for widespread commercial use. This project aims to overcome these barriers by developing a high‑resolution spatial light modulator based on high‑aspect‑ratio silicon pillars
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details Supervisors: Dr Oksana Trushkevych and Prof Tony McNally Research area and project description: Develop scalable acoustic methods to structure advanced polymer composites for lightweight, low‑carbon
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to strengthening UK capability in strategically important high-voltage semiconductor technologies, supporting decarbonisation, grid modernisation, and advanced manufacturing. The successful candidate will develop
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the University of Warwick. Project outline: Modelling light-driven processes and charge transfer across molecule-metal interfaces is instrumental for the development of next-generation molecular optoelectronic
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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
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: Imagine a surgeon operating remotely through a robot—what if the network slows at a critical moment? Even tiny delays can risk patient safety. This PhD project develops new AI approaches to predict network
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sensors - if we can control and tune their properties. You will develop and use top-of-the-line machine learning models to predict the sensor response of these materials under realistic conditions