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- See advert for details Deadline: 13 April 2026 Supervisors: Dr Subhash Lakshminarayana (Lead) and Dr Michael Faulkner Safeguarding Power Grid Stability in the Age of Artificial Intelligence (AI) Data
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atomic and macroscopic length scales. Developing a predictive, fundamental theory linking stress to measurable magnetic signals will widen the applicability of mNDT and support the design of next
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Materials & Cells (BMAC) group at WMG, University of Warwick are looking for a well-qualified PhD on linking fundamental battery research and industrial scale up & characterization. The BMAC groups covers
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molecular weight reduction, cross-linking, and morphological alterations. Additionally, degradation of the fibre–matrix interface can occur, further compromising the mechanical integrity of these composites
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of pre-eclampsia Research area and project description: Pre-eclampsia affects 1 in 10 pregnancies, yet diagnosis remains uncertain. This PhD will integrate clinical data and blood biomarkers to improve
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: Interested candidates should submit a full formal application. Guidance and the application form are here: click the 'Apply' link above.
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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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-effort services rather than guaranteed real-time performance, and the transmission of multi-modal data streams in telesurgery—such as point clouds, haptic feedback, and audiovisual signals—places
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of spins that could form the basis of future low-power data storage devices. However, real skyrmions are three-dimensional and can twist, stretch, or deform when trapped by material defects - behaviour
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physics-based and data-driven methods to support the design and scale-up of these systems. This approach will reduce the need for costly experiments, improve scale-up predictions, and provide confidence