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Optimization-based control explores the use of optimization algorithms for feedback control of dynamical systems. For example, model predictive control (MPC) is a widely used optimization-based control method
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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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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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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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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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. 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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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