Postdoctoral researcher in ML for dynamical systems representation, prediction, and state-estimation

The Nonlinear Systems and Control group is seeking a talented and ambitious Postdoctoral Researcher to develop machine learning-enabled approaches for predictive modelling and state estimation for fundamental applications within physical sciences.

Your role

The main research responsibilities involve building cutting edge machine learning techniques for sequential data modelling, including Physics-informed Machine Learning and Koopman Operator-based representation framework, towards building interpretable predictive models for complex multi-physics dynamical systems as well as towards designing observer-based state estimators from output timeseries data measurements. The research also involves development of uncertainty quantification techniques for the learnt models.

You will also have opportunities to contribute to open-source computational tools and datasets, teach master-level courses, and advise doctoral students. You w...