Assistant Professor in Dynamics and Computational Design of -Tech Systems

Assistant Professor in Dynamics and Computational Design of High-Tech Systems

Would you like to be part of a vibrant and collaborative academic community, contributing to innovations with real-world impact? Join us in shaping the future of 'dynamics & control' in the heart of the high-tech region of the Netherlands! The Dynamics and Control section of the Mechanical Engineering department at Eindhoven University of Technology is seeking a passionate and talented Assistant Professor to expand its team.

The mission of the Dynamics and Control (D&C) section is to perform research and train the next generation of students in understanding and predicting the dynamics of complex engineering systems to develop advanced control, estimation, planning, and learning strategies which are at the core of the intelligent autonomous systems of the future: 'designing and realizing smart autonomous systems for industry and society'.

High‑tech and cyber‑physical systems, autonomous articulated vehicles, fully automated industrial value chains, smart energy systems, intelligent medical devices, automated transportation networks, and soft robotics are key examples of the broad application domain of the D&C section. The design of these systems requires a thorough understanding of their underlying dynamics. Therefore, the first focal point of our research is on data‑based and first‑principle‑based modeling, model complexity management, computational and experimental dynamic analysis, and design of complex multi‑physics, multi‑disciplinary, and cyber‑physical engineering systems.

Building on this foundation, our second focal point is ‘making autonomous systems smarter’. To this end, we develop model‑based and data‑based sensing, planning, diagnostics, control technologies as well as computational and experimental methods to provide autonomous systems with the intelligence needed to guarantee performance, robustness, safety, security, and sustainability.

The candidate is expected to have a strong computational profile, complemented by expertise in dynamics and/or optimization. The research should contribute to the development of next‑generation methodologies from an integrated co‑design perspective, bridging mechanical system design and control.

We are looking for a candidate who will strengthen our section in at least one of these research themes:

This topic aims to move beyond the traditional separation between mechanical system design and control engineering by adopting fully integrated co‑design methodologies. System‑level specifications serve as the starting point for the simultaneous optimization of system architecture, including structural components, actuator and sensor placement, and control strategies.

The objective is to establish tightly coupled design workflows that enable efficient development of high‑performance mechatronic systems, improving both performance and development speed while reducing iteration cycles.

This topic explores the use of generative AI to enhance and partially automate engineering design processes. By combining data‑driven methods with physics‑based modelling, these approaches enable rapid exploration of design spaces and the discovery of novel system configurations. The focus is on developing hybrid frameworks that integrate machine learning with first‑principles models to support intelligent and automated design of high‑performance systems.

This topic explores the development of computationally efficient and accurate methods for simulation and optimization of complex dynamical systems. The focus is on hierarchical approaches bridging low‑ and high‑fidelity representations, reduced‑order and surrogate techniques, and hybrid physics‑based and data‑driven methods for multi‑physical dynamical systems. The work emphasizes real‑time capable simulation and scalable computational frameworks, leveraging advanced parallelization strategies on CPU and GPU architectures to enable large‑scale system analysis and integrated co‑design.