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About the project: Advanced Monte Carlo methods for glassy dynamics and complex materials Supervisor: Dr Michael Faulkner, University of Warwick Glasses are materials that combine macroscopic solid
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training on computational materials modelling and gain experience with cutting-edge quantum transport simulation methods, conduct a vibrant research project with high publication potential and have an
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validation methods employed for battery systems created for electric vehicles, aerospace or stationary storage. This PhD will aim to deliver a new validated methodology for scientifically assessing lithium-ion
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ignition products, with future opportunities expected in data storage devices and electrical contacts to supply the demand for higher computational power for AI driven technologies. However, the limited
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-ray Computed Tomography (XCT) has evolved into a significant "big data" challenge, with a single scanner easily generating over 10TB of data annually. The sheer volume of this structured data creates
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About the project: Supervisor: Professor Nicholas Hine, University of Warwick This project uses cutting-edge computational and machine learning methods to accelerate catalyst discovery for fuel cell
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. The PhD researcher will advance scalable wet‑etch fabrication methods to create next‑generation THz modulators with applications in security screening and early cancer detection. Terahertz (THz) radiation
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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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Electronics, will use computational simulations to study how thin films form during flowable chemical vapor deposition (FCVD), a process used to build advanced semiconductor devices. Unlike traditional CVD
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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