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Deployment The PhD programme offers: Training in the theory for solar energy technologies, experimental measurement and evaluation techniques, tools for modelling and predicting PV generation. Opportunities
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affect ignition behaviour. You’ll use advanced tools such as chemical kinetic modelling, multi-dimensional CFD simulations, and collaborate closely with experimental researchers. You will receive
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This PhD opportunity at Cranfield University explores how next-generation AI models can be embedded within resource-constrained electronic systems to enable intelligent, real-time performance
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position initially and is expected to be held full time and in person. You will join the CNNP Lab, which is well supported with recent funding of over £3M. The lab is based in the School of Computing
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, including high throughput experimentation, programming (e.g. in LabView, Matlab) and numerical modelling. They will be joining a thriving, inclusive Chemistry department with excellent facilities
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to study corrosion, cracking and mechanical degradation, develop advanced computational models using modern C++ and high-performance computing to simulate material behaviour over a 100+ year timespan. This
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of the project is to 1) develop computational pipelines for image analysis and physical analysis of cell shape trajectories, and for combined morpho-molecular analysis of cell shape together with molecular markers
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integrates machine learning and statistics to improve the efficiency and scalability of statistical algorithms. The project will develop innovative techniques to accelerate computational methods in uncertainty
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: Coordination Layer: Formulate passivity-based conditions that guarantee agents—modelled as general nonlinear systems—synchronize their outputs or follow desired collective patterns purely through local
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behaviours of thin foils in vacuum and inert environments will be explored. Based on the results, a constitutive material model including the creep effect (time, temperature and load dependencies) will be