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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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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
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), where heat is stored/released through reversible chemical reactions. This project focuses on NaOH water TCES systems, which use cheap, abundant materials [1]. We will develop modelling tools that combine
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that is still poorly understood. This project will develop advanced computational models to simulate a new imaging technique called electron ptychography, which can map magnetic fields in 3D at nanometre
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Energy’s Natural Hazards R&D Team, this project will utilise and develop state-of-the-art space simulations to probe past, present and future events to constrain extreme value distributions spanning hundreds
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therefore paramount, but traditional simulations are plagued by the same slow relaxational dynamics. Through collaboration across Engineering, Statistics and Chemistry, this project will develop state