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Modelling and Identification of Spatiotemporal Systems

   Department of Automatic Control and Systems Engineering

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  Prof Visakan Kadirkamananthan  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

The increased sensing mechanisms and data gathering in non-Engineering disciplines such as life science, medicine and the social sciences have resulted in a demand for more sophisticated quantitative analyses. Often, these analyses demand a greater understanding of the systems behavior, particularly from a dynamical systems perspective, such as causality and predictability. One class of data are of spatio-temporal in nature where measurements are made at different spatial locations and over time.

The aims of this project is to derive novel spatio-temporal modeling methods demanded by specific application case studies that have spatio-temporal data. It will build on the group’s success in developing such models for intracranial EEG signals, novel array sensor signals of ECG and EMG, and dynamics of social conflicts. The spatiotemporal model estimation will be based on a statistical inference framework and will utilise Kalman filtering concepts.

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 mathematical and computational skills. Familiarity with estimation theory such as least squares and Bayesian inference, numerical analysis and state space control methods is desirable.
Full details of how to apply can be found here:
Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive:
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