29 algorithm-development "https:" "Simons Foundation" PhD positions at University of Warwick
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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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Funding for: UK Students Research area and project description: Develop scalable acoustic methods to structure advanced polymer composites for lightweight, low‑carbon technologies. This PhD explores
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develop next-generation power converters for mission-critical applications: from hyperscale AI hubs to MW-scale EV charging and smart grids. The UK’s Modern Industrial Strategy and commitment to Net Zero
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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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: 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