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Field
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Deadline: Applications accepted all year round Details The aim of this project is to develop scalable and efficient techniques and algorithms for localisation in different environments, based on data in
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to the advancement of digital imaging technique and computing power. In this project, high speed stereo imaging for flame studies developed in Prof. Zhang’s research lab will be further developed for more quantitative
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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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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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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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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