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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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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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information extraction. Advanced algorithms will be developed to obtain useful information such as the 3D flame topology and spread velocity. The candidate should have a good background in mathematics and they
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practices produced with the help of computer algorithms challenge, subvert and threaten the modernist concept of the author. AI generated creative practices have the capacity to seriously disrupt established
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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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. The ultimate goal is to develop theory and methods for the construction of low-complexity invariant sets, using computationally tractable algorithms. Funding Notes This is a self-funded research project. We
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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
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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