29 web-developer-"https:" "https:" "https:" PhD scholarships at University of Warwick
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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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effectively and completely re-used as functional photo-ink to allow for 3D-print-to-re-printing. You will join our team efforts to develop new 3D printing inks for light-based printing of the next generation of
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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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modes of molecular recognition and new catalytic strategies that are not easily achievable through established non‑covalent interactions. Despite the rapid theoretical and conceptual development
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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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. Specifically, we develop small molecule photo(cyclo)addition reactions that allow for the efficient formation of covalently bound reaction products under (visible) light irradiation. Importantly, the thus formed
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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