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be paid to the relationships between the energy source (UV or EB), conversion, network structure and final properties. The work will focus primarily on the study of model polymerisable formulations
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characterisation, and computational modelling to investigate the relationship between matrix properties and osteogenesis. This will contribute to deciphering the importance of mechanical stimulation of the cellular
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at experimentally informed microstructural features. You will combine first-principles modelling and machine-learning approaches to develop predictive simulations of hydrogen behaviour in compositionally complex Fe
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subsequently be assembled into complex aerostructures using welding. This provides significant opportunities for more efficient and cost-effective aircraft manufacturing. However, welded composite assemblies
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applications. As AI models continue to increase in size and computational complexity, the demand for high-bandwidth, low-latency, and energy-efficient interconnects has become one of the key technological
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machine learning methods for advanced and in situ materials characterisation. Modern characterisation techniques generate increasingly complex datasets describing microstructure, crystallography, phase
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developed mouse models and human tissue processing pipelines. The candidate will learn and use cutting edge immunological methods (high parameter flow cytometry, multiplex imaging, single cell and spatial
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Despite significant advances in numerical techniques and computing hardware, the high computational cost of large-scale 3D computational fluid dynamics (CFD) modelling remains a major challenge. A
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novel methodology for the analysis of single-cell (multi)-omics data by incorporating existing biological knowledge into machine learning models. You will join a collaborative and internationally-oriented
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compositionally complex circular steels. As a PhD researcher, you will: Perform Density Functional Theory (DFT) calculations to model hydrogen-tramp element co-segregation at grain boundaries and phase boundaries