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Sequential Monte Carlo Methods for Bayesian Inference in Complex Systems


   Department of Automatic Control and Systems Engineering


About the Project

This project will focus on the development of Bayesian inference methods such as sequential Markov Chain Monte Carlo (MCMC) methods for filtering and smoothing of states and parameters of complex nonlinear systems. The project will also design likelihood free methods for systems with many interactions between their components. Different applications will be considered with large scale systems.


Funding Notes

This is a self-funded research project.
We require applicants to have either an undergraduate honours degree (1st) or MSc (Merit or Distinction) in a relevant science or engineering subject from a reputable institution.
Prospective candidates for this project should have an MSc, MMath or MEng in mathematics, statistics, physics, aerospace engineering, signal processing, electrical engineering or a related subject.
Full details of how to apply can be found at the following link:
View Website
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive: View Website

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