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an interdisciplinary program that brings together Warwick Medical School and partners from the School of Engineering and Industry, combining machine learning and computational modelling to solve a
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From electric vehicles to grid-scale energy storage, the future of clean energy relies on breakthroughs in battery technology. This PhD will develop cutting-edge terahertz spectroscopy methods
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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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heterostructures for functional quantum devices. The candidate will receive a broad training on computational materials modelling and gain experience with cutting edge quantum transport simulation methods, conduct a
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, and the sector urgently needs reliable methods to detect and understand it. At present, however, plating remains extremely difficult to measure non-destructively in industry-relevant cell formats
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machine learning and AI methods to develop clinical decision support systems for high-flow nasal cannula therapy. The project: Concerns around the possible negative consequences of delayed escalation
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PhD Studentship: Advancing solid-state battery electrolyte manufacturing with terahertz spectroscopy
compounded by the lack of rapid inspection methods capable of assessing microstructural homogeneity, which is increasingly important for product-scale electrolytes. The PhD candidate will join a dynamic
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to enhance performance & longevity; and interplay between degradation modes. Characterization methods will include bespoke microscopy facilities, in-house and Synchrotron & neutron operando studies, and
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high flow nasal cannula in patients with acute hypoxemic respiratory failure: A computational study", Respiratory Research, 2025. pdf [2] H. Shamohammadi, S. Saffaran, R. Tonelli, V. Chiavieri, G
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, it is implemented via three-loop CF cascaded control. The project aims to develop: • Robust and fault-tolerant CF estimator overperforming Level 5 EV automation expectations under EMB parameters