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Field
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’. Warwick University is renowned for its high-quality research and a thriving PhD program. This strong research culture enhances both the PhD student’s experience and the demand for our graduates. This PhD
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additives in lithium-ion battery (LiB) electrodes. With the exponential growth in demand for high-performance batteries, the need for scalable, high-quality CNT production has never been greater. Chemical
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disturbances. This programme of doctoral work will investigate the prevalence of iron deficiency/ iron deficiency anaemia in high performing athletes and assess the effectiveness of a novel oral iron supplement
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local gas/liquid phase conditions. Whilst direct simulations of breakup are possible, computational cost is high, restricting applications to small sections of geometry and for modest run times
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the PhD student in high-performance computing, computer programming, applied mathematics, fluid mechanics, mathematical modelling and data analysis for large datasets -of the order of 100 Terabytes
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-value reinforcements in their short and randomly aligned form. A key challenge to the effective reintegration of recycled carbon and glass fibres into high-performance products lies in achieving scalable
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(conductivity, heat capacity, flame resistance). Advanced finite element modelling will then correlate microstructural features to heat-transfer performance. The candidate will design and build a burner-rig test
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are essential to meet the demands of next-generation applications. This PhD project offers an exciting opportunity to pioneer separator-free battery architectures using ultrathin, high-performance coatings made
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: Computational Modelling: Employing simulation tools (e.g., GEANT4, light transport) to explore novel metamaterial designs, predict performance, and optimise key parameters such as timing resolution, light yield
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state-of-the-art high heat flux testing, simulating the extreme environments of fusion reactors. Harness advanced computational tools to model complex particle-material interactions and predict material