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algorithms that would allow the delay and/or suppression of hysteresis effects in dynamic stall through the use of control surfaces, for example, allowing the safe recovery of aircraft from post-stall
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adapted based on the abilities and needs of patients. Moreover, automatic intelligent algorithms will be developed in to make the control intuitive, natural and adaptive. Such that the model can learn new
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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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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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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