Inference in Dynamical Systems


The project involves developing novel methods for state inference in complex dynamical systems with a later focus on the estimation of joint model and state from observations of the state over time. For example, one may have observations in time from a weather system and be interested in both estimating the relevant climate model describing the system as well as forecasting future states of the system. In very high-dimensional systems (like weather systems), computational complexity versus estimation accuracy and uncertainty quantification become key considerations in algorithm design and analysis. The student will look at novel methods for inference in these settings that consider these factors with an emphasis on rigorous algorithm development and performance characterisations. Expected outcomes will be methods as just detailed with software and published articles to be produced. The student will likely work closely with practitioners in application fields (like climate scientists) as well as with data scientists and mathematical statisticians and machine learners with the aim of producing practical methods that may be tested in real systems.


  • Basic statistics, math, and/or computer science skills with applications in data science, analytics and/or machine learning.
  • Research experience at the level of a dedicated honours-type project is necessary.
  • Experience in programming in Python and MATLAB will be beneficial but more general programming skills are sufficient.


The project will be carried out in collaboration with Dr Adrian Bishop from UTS.

Edwin V. Bonilla
Principal Research Scientist

My research interests include probabilistic modelling and inference, Gaussian processes and transfer learning.