The drive to more power-efficient, light-weight and/or performant systems results in more and more nonlinear dynamic system behavior. An example of nonlinear behavior can be retrieved in mechatronic systems, e.g. due to friction and actuation nonlinearities, or in large-scale mechanical systems where nonlinearities often present themselves at the interconnection of the subsystems. Data-driven nonlinear modelling approaches are often highly complex, requiring an expert user, and resulting in highly complex model descriptions. However, for a successful adaptation in a wide range of applications, data-driven nonlinear modelling tools and the resulting nonlinear models need to be interpretable by the practitioners.

The aim of this project is to integrate and extend the recently developed machine- and deep learning tools for the identification of nonlinear dynamical systems with a specific focus on (deep) neural networks. Whereas (deep) neural networks have proven their capabilities in many classification and regression tasks, the use of these estimation methods have been less well studied for the purpose of modelling nonlinear dynamical engineering systems. This PhD project aims to surmount these challenges by establishing an innovative synergy between the machine learning and the nonlinear system identification communities. The goal is to develop computationally efficient model learning approaches capable of supporting control synthesis and system design with special attention towards explainable AI approaches. The flexibility of the machine learning framework in defining learning objectives (aim-relevant estimation) and its ability to facilitate optimal recovery of structural relationships (model structure selection) provide novel perspectives in terms of developing dedicated methods to solve the limiting problems the current nonlinear identification theory.

Tasks:

• Study the literature of machine learning, nonlinear identification and modelling.
• Development of (control-oriented) interpretable data-driven nonlinear modeling approaches.
• 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 strong background in systems and control, mathematics, statistics and signal processing. Preferably you finished a master in Systems and Control, (Applied) Physics, (Applied) Mathematics, Information Technologies, Electrical Engineering or Mechanical Engineering.
• You have good programming skills and experience (Matlab and/or Python is 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).

We offer you:

• An exciting job in a dynamic work environment
• The possibility to present your work at international conferences.
• A full time appointment for four years at Eindhoven University of Technology (www.tue.nl/en)
• The salary is in accordance with the Collective Labour Agreement of the Dutch Universities, increasing from € 2,325 per month initially, to € 2,972 in the fourth year.
• An attractive package of fringe benefits, including end-of-year bonus (8,3% in December), an extra holiday allowance (8% in May), moving expenses and excellent sports facilities.