27 developer-"https:" "https:" "https:" "Univ" PhD positions at University of Warwick in United Kingdom
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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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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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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