Data-driven and kernel-based analysis of complex dynamical systems

The main focus of this project is the data-driven analysis of complex dynamical systems exhibiting multiple time scales. Based only on simulation or measurement data, dominant dynamics or modes can be extracted, which are then, for instance, used for dimensionality reduction, the detection of metastable or coherent sets, system identification, or control. Due to the sheer size of the data sets, kernel-based approaches might be required to mitigate the curse of dimensionality. The successful candidate will develop, optimize, and implement novel methods to analyse high-dimensional time-series data in order to gain insight into the characteristic properties of the underlying system. Of particular interest are molecular dynamics problems (computation of stable conformations, analysis of protein folding processes), fluid flow problems (detection of coherent sets), and quantum mechanics problems (relationships with transfer operators, stochastic formulations of quantum mechanics, exploitation of symmetries/antisymmetries).

The successful candidate will receive comprehensive research training related to all aspects of the research and opportunities to participate in conferences, workshops and seminars to develop professional skills and research network.

Supervisor: Dr Stefan Klus

This is a minimum 3 year project. We are able to offer this opportunity starting in October 2022.

Entry requirements

Applicants should have a minimum of a first class honours degree in mathematics, the physical sciences or engineering. Preferably applicants will hold a MMath, MPhys or MSc degree, though exceptional BSc students will be considered.

English language requirements: IELTS Academic 6.5 or above (or equivalent) with 6.0 in each individual category.

How to apply

Applications should be submitted via the Mathematics PhD programme page on the "Apply" tab.

Please state clearly the studentship project at you would like to apply for.