PhD on Combined Physics- and Machine Learning-Based Modeling of Complex Dynamical Systems Eindhoven University of Technology in Netherlands

This PhD position at Eindhoven University of Technology focuses on the combined use of physics-based and machine learning-based modeling for complex dynamical systems, with a particular application to semiconductor equipment for heterogeneous integration. The project is situated within the Dynamics and Control section of the Department of Mechanical Engineering, a vibrant research community dedicated to advancing intelligent autonomous systems for industry and society. You will join a team of two PhD students working on hybrid modeling frameworks that synergize the strengths of physics-based models (interpretability, generalizability) and machine learning models (accuracy, data-driven insights) to create highly predictive dynamical models for high-tech industrial applications.

The research will address the limitations of traditional physics-based models in multi-physics contexts and leverage AI and machine learning to enhance predictive capacity, while maintaining interpretability and generalizability. The core industrial use case involves collaboration with ASMPT, a leading company in semiconductor equipment, providing opportunities for both academic and industrial research experience. You will have access to graduate courses at the Dutch Institute of Systems and Control (DISC) and the Engineering Mechanics Research School (EM), and collaborate with industry partners in the Brainport region as well as academic researchers worldwide.

Supervision is provided by Professor Nathan van de Wouw, an expert in dynamical systems and control. The position offers full-time employment for four years, with a competitive salary (scale P: €3,059–€3,881/month), year-end bonus, vacation pay, pension scheme, paid parental leave, commuting and internet allowance, and a tax compensation scheme for international candidates. You will spend 10–15% of your time on teaching tasks and benefit from high-quality training programs, technical infrastructure, and support services including staff immigration assistance.

Applicants should hold a master’s degree in dynamical systems, mechanical engineering, electrical engineering, AI and machine learning, or applied mathematics, and possess a strong background in systems theory and machine learning. Affinity with mechatronics or robotics is preferred, and proficiency in Matlab or Python is expected. The ability to work independently and collaboratively, as well as fluency in English, are required.

To apply, submit a complete application online including your CV, publication list, contact information for three references, a statement of interest, diplomas and transcripts, and a brief description of your MSc thesis or relevant research projects. Applications sent by email or post will not be processed. The vacancy will remain open until filled, with a formal deadline of January 4, 2026.

For further information, contact Professor Nathan van de Wouw ([email protected]) and always cc Mrs. Geertje Janssen-Dols ([email protected]). Additional HR contacts are available for queries about employment conditions. Join TU/e and contribute to cutting-edge research in dynamical systems, control, and machine learning, while building a strong academic and industrial profile in a leading international university.