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Bayesian system identification in nonlinear engineering dynamics School of Mechanical, Aerospace and Civil Engineering PhD Research Project Directly Funded Students Worldwide Prof Keith Worden
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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems School of Electrical and Electronic Engineering PhD Research Project Self Funded Prof Lyudmila Mihaylova Application Deadline
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Bayesian system identification in nonlinear engineering dynamics
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Interview Motivated in learning new methodologies and applying new knowledge Essential Interview Knowledge of the approximate Bayesian machine learning (e.g. MCMC) (assessed at: Application form/Interview
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of rail with wider city and regional transport networks. A focus of this work is the application of optimisation techniques (e.g. evolutionary algorithms, or Bayesian techniques) to identify high performing
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main project by addressing specific case studies or specific targeted techniques. The main tools to be used will come from the discipline of Machine Learning, particularly those based on Bayesian methods
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environments and inaccurate prior maps, to name a few. In order to cope with these challenges different methods will be developed. Knowledge of Bayesian methods for sensor data fusion, mapping and multiple
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campaigns including programmed screening or Bayesian optimisation. You will characterise the resulting materials, in terms of their properties and performance for an intended application. Sustainability will
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, including sequential Monte Carlo methods, Gaussian processes and Bayesian compressed sensing. Applicants from different backgrounds are encouraged to apply depending on the specific nature of the project
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year round Details This research is aimed at developing scalable Bayesian approaches able to solve complex and high dimensional problems with multiple objects and multi-sensor data. One such problem is