23 data-"https:" "https:" "https:" "https:" "https:" "https:" "SciLifeLab" scholarships at University of Warwick
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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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Modulation and Imaging Experiments: Data Analysis and Design Optimisation: Scholarship: The award will cover the UK tuition fee level, plus a tax-free stipend, currently £21,805, paid at the prevailing UKRI
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their skills and interests in this research area to https://warwick.ac.uk/fac/sci/eng/postgraduate/funding/ot_epsrc/app/ via the above 'Apply' button. If this initial application is successful, we will invite
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not essential How to apply: Interested candidates should submit an expression of interest by sending a CV and supporting statement outlining their skills and interests in this research area to https
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Modelling (WCPM) . Find out more about the research group at https://koczorbenda.wordpress.com/ Requirements and eligibility: Applicants must have, or be predicted to obtain, a good degree (2.1 or 1st class
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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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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