This open PhD position aims to develop a framework that enables the identification of nonlinear mathematical models with pre-specified properties that are physically interpretable. To achieve this, the PhD candidate will exploit recent developments from the fields of machine learning, tensor algebra, system identification, and system theory to come to interpretable and stable models of complex dynamical systems.
- Study the literature of nonlinear system identification, machine learning, tensor algebra, and system theory.
- Development of interpretable, data-driven nonlinear modeling approaches using tensor decompositions, nonlinear state-space identification techniques, and constrained Gaussian Processes.
- Analysis of the stability of the identified models in a time-efficient way.
- Enforcement of the stability during the identification step.
- Dissemination of the results of your research in international and peer-reviewed journals and conferences.
- Writing a successful dissertation based on the developed research and defending it.
- Assume educational tasks like the supervision of Master students during courses, internships, and graduation proJjects.
- talented and enthusiastic young researcher.
- experience with or a strong background in systems and control, mathematics, and signal processing. Preferably, you finished a master’s in Systems and Control, Mechanical Engineering, (Applied) Physics, (Applied) Mathematics, Computer Science, or Electrical Engineering.
- good programming skills and experience (Matlab and/or Python are an asset).
- organized, autonomous, a team player, and have strong communication skills.
- creative and ambitious, hard-working, and persistent.
- good command of the English language (speaking Dutch is an advantage but is not required).