Are you an enthusiastic researcher with a proven track record in the area of dynamics and control? Would you like to contribute to the challenging field of digital twinning for monitoring and control? You may be the candidate we are looking for!The Dynamics and Control Section
The mission of the Dynamics and Control Section
within the Mechanical Engineering department
is to perform outstanding research and make key contributions to future societal challenges and industrial challenges in the fields of high-tech systems, mobility, sustainability and health. We train next-generation students on the topic of understanding and predicting the dynamics of complex engineering systems in order 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. Our section has a broad funding base and a strong track record in attracting industrial funding and in engaging in international collaborations.
The Dynamics and Control section currently consists of a diverse and committed team of 21 staff members, 6 post-doc and 35 PhD researchers.Our research
Autonomous vehicles, fully automated industrial value chains, high-tech systems, collaborative robots in unstructured environments, intelligent medical devices, automated transportation networks, soft robotics, together with sustainable automotive technology are key examples of the broad application domain of the Dynamics and Control section.
The design of these systems requires a thorough understanding of their underlying dynamics. Therefore, the first focal point of our research is on both data-based and first-principle-based modelling, model complexity management, and dynamic analysis of complex, multi-physics, multi-disciplinary and cyber-physical engineering systems. Digital Twinning hence is a core focal point for the Dynamics and Control section.
Building on this foundation, our second focal point is on 'making autonomous systems smarter'. To this end, we develop both model- and data-based sensing/perception, planning, and monitoring and control technologies to provide autonomous systems with the intelligence needed to guarantee performance, robustness, and safety. Hence, digital twin development must be geared towards usage in the scope of monitoring and control.
Combining the investigation on both dynamics and control theory in one section allows to take on these challenges standing in a privileged position. In particular, it enables us to educate uniquely skilled engineers and researchers as well as to valorise our research together with the high-tech, automotive, health-care and energy sectors. While the disciplinary focus of the Dynamics & Control section is on 1) Dynamical Systems and 2) Control (where the combination is a unique selling point), the section focusses on the following main themes:
Digital Twinning for Monitoring and Control
- Collaborative and Autonomous Robotics.
- Automotive Systems and Smart Mobilit..
- Intelligent High-tech Systems Design.
- Manufacturing Systems and Smart Industry.
To strengthen our section we have an open tenure-track Assistant Professor position in the area of Digital Twinning for Monitoring and Control. Digital Twinning involves the construction of high-fidelity models of highly complex engineering systems, containing multi-physical, multi-disciplinary, and even cyber-physical and networked, aspects. The complexity of such systems blocks a purely first-principles modelling approach and hence a hybrid model- and data-based approach towards modelling, including machine learning techniques, is needed to strike an appropriate balance between model accuracy and complexity, while retaining interpretability and generalizability.
Digital twins hold great promise for online, real-time usage. In this scope, numerous (engineering and system-theoretic) applications of digital twinning can be found in the scope of system monitoring: model updating, fault and anomaly diagnostics (including fault detection, isolation, estimation), root cause analysis and predictive maintenance. In addition, digital twins can support superior decision-making in the scope of fault mitigation, planning and control.
This position will focus on the development of theories and tools for exploiting digital twins for online monitoring and control purposes.