PhD in Diagnostics for Interconnected Complex Dynamical Systems Eindhoven University of Technology in Netherlands

PhD opportunity in Diagnostics for interconnected complex dynamical systems at Eindhoven University of Technology (TU/e), Department of Mechanical Engineering.

This project sits in the Dynamics and Control section and focuses on the reliable operation of complex engineering systems such as semiconductor equipment. The research aims to develop innovative monitoring algorithms for fault detection and isolation, hierarchical diagnostic tools, distributed observer design, and data-driven / machine learning methods for interconnected dynamical systems.

The position is part of the broader project Holistic Design Automation for Semiconductor Manufacturing Equipment, with collaboration opportunities involving ASML, the Brainport high-tech region, and researchers across TU/e departments including Electrical Engineering and Mathematics and Computer Science. The role offers access to graduate courses at the Dutch Institute of Systems and Control (DISC) and a strong academic-industrial environment.

Eligibility: applicants should hold a master’s degree or equivalent in systems and control, mechanical engineering, electrical engineering, or applied mathematics. A strong background in mathematical systems and control theory is required, along with interest in hybrid dynamical systems, observer design, learning techniques, and optimization. Programming experience in Matlab or Python is expected, and fluent English is required.

Funding: full-time employment for four years, with salary in scale P (€3,059–€3,881 per month), plus year-end bonus, vacation pay, and standard Dutch university benefits. International candidates may also benefit from the 30% tax facility.

Application: submit a complete online application via the TU/e vacancy portal before 2026-07-17. The application must be uploaded as a single PDF and include a CV, publication list, three references, motivation letter, diplomas/transcripts, and a short description of MSc thesis or relevant projects.

Supervisors: Prof. Nathan van de Wouw, Prof. Tom Oomen, and Dr. Michelle Chong.