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this issue and we could use obtain data-driven models using machine learning algorithms such as artificial neural networks, reinforcement learning, and deep learning. A typical caveat of data-driven modelling
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using their high performance computational mechanics algorithm Alya, by coupling solid mechanics with electrophysiology. This project aims to personalise this model using medical images (MRI) collected
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optimise the algorithms for optimal process control. The research will benefit from the available experimental facilities including laboratory-scale digesters, excellent analytical facilities, expertise in
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of the system is within the set at some time, then it is guaranteed to remain within the set for all future times. Therefore, being able to characterize and compute these sets is of prime importance when
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between the brain signals of different subjects. The aim of this project is developing new adaptive and machine learning algorithms to successfully decode brain signals across subjects. The prospective
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finite element modelling to simulate the deformation of microstructures, novel crack propagation simulation techniques and scale-transition algorithms. The model will be informed and validated using full