Modelling and Identification of Spatiotemporal Systems

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 a number of related projects are 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. The projects are carried out in collaboration with University of Sheffield Departments in relevant application areas and other International Institutions.

The projects will require candidates to have excellent mathematical and computational skills. It will be an advantage if the candidates have familiarity with estimation theory such as least squares and Bayesian inference, numerical analysis and state space control methods.

Applicants can apply for a Scholarship from the University of Sheffield but should note that competition for these Scholarships is highly competitive. It will be possible to make Scholarship applications from the Autumn with a strict deadline in late January/early February. Specific information will appear: View Website