Project Description

Systems and control engineers aim to master increasingly complex dynamical systems while including stronger performance, operational and energy constraints. As model-based control design remains the dominant paradigm, this results in an increasing need for nonlinear modeling. However, model interpretability and generalization capabilities form important roadblocks for a wide adaptation and applicability of nonlinear system identification methods. This ERC-funded project aims to tackle these challenges.

Strong prior knowledge is given by existing models, provided by system designers and engineers, even though they do not capture all the nonlinear dynamics of the real-life system. These models are currently not accounted for during data-driven modelling. This project aims to develop a comprehensive nonlinear system identification framework to obtain accurate and interpretable models of measured complex system dynamics by completing an approximate pre-existing model through black-box nonlinear system identification. New theory and algorithms are put in place to 1) provide model structures, algorithms and theory that flexibly interconnect the pre-existing model and the data-driven completion 2) ensure that data-driven completion models are interpretable and preserve key system theoretic aspects 3) data-driven experiment design strategies to detect, quantify and localize model errors at low experimental cost. The resulting system identification methodologies are applicable over a wide range of engineering disciplines (mechanical, electrical, biomedical) and provides system engineers with the necessary insight to guide them towards better solutions for tomorrow's industry.

Tasks

• Study the literature of machine learning, nonlinear system identification and data-driven modelling.
• Development of (control-oriented) interpretable data-driven nonlinear modeling approaches for model completion.
• Stochastic analysis of consistency and convergence of the results and empirical validation of the techniques on complex physical/chemical and/or electrical/mechatronic systems.
• Exploration of the steps of the identification cycle for the developed methods from experiment design to verification of model completion (validation).
• 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 and internships.

We are looking for a candidate who meets the following requirements:

• You are a talented and enthusiastic young researcher.
• You have experience with or a background in systems and control, mathematics, statistics, and signal processing. Ideally, you have experience with system identification and/or data-driven modelling.
• Preferably you finished a master's in Systems and Control, Mechanical or Electrical Engineering, (Applied) Physics or (Applied) Mathematics.
• You have good programming skills and experience (Matlab and Python are an asset).
• You have good communicative skills, and the attitude to partake successfully in the work of a research team.
• You are creative and ambitious, hard-working, and persistent.
• You have good command of the English language (knowledge of Dutch is not required).

• A meaningful job in a dynamic and ambitious university with the possibility to present your work at international conferences.
• A full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months.
• To develop your teaching skills, you will spend 10% of your employment on teaching tasks.
• To support you during your PhD and to prepare you for the rest of your career, you will make a Training and Supervision plan and you will have free access to a personal development program for PhD students (learning process).
• A gross monthly salary and benefits (such as a pension scheme, pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labor Agreement for Dutch Universities.
• Additionally, an annual holiday allowance of 8% of the yearly salary, plus a year-end allowance of 8.3% of the annual salary.
• Should you come from abroad and comply with certain conditions, you can make use of the so-called '30% facility', which permits you not to pay tax on 30% of your salary.
• A broad package of fringe benefits, including an excellent technical infrastructure, moving expenses, and savings schemes.
• Family-friendly initiatives are in place, such as an international spouse program, and excellent on-campus children day care and sports facilities.