The TU/e offers a PhD position with the DIGITAL TWIN research program. This NWO AES Perspectief programme is a five-year comprehensive research programme on the development of digital twin and digital twinning methods, financed by the Dutch Research Council (NWO) within the domain of Applied and Engineering Sciences (AES). This collaborative programme involves six universities: University of Groningen, Eindhoven University of Technology, TU Delft, University of Twente, Leiden University and Tilburg University and ten industrial partners and two research institutes.
The development of reliable and agile digital twins of high-tech systems and materials is key to enabling shorter time-to-market, zero-defect and flexible manufacturing systems with accurate predictive maintenance. This crucial development is currently hampered by the lack of synergy between model-based engineering and data-driven/artificial intelligence approaches. The DIGITAL TWIN program will develop key-enabling technologies for full digitization of the value chain of high-tech systems and materials by the integration of data-driven learning approaches and model-based engineering methods.
One of the projects (involving three PhD projects) within the DIGITAL TWIN program focusses on Autonomous Process & Control Reconfiguration and Optimization. In this scope, the open PhD position at TU/e is on
Automated data-based performance optimization of control systems.
Control strategies for complex high-tech systems (such as precise positioning, thermal management, vibration isolation) are typically designed based on models of the underlying dynamics and/or based on measurement of these dynamics before delivery of the equipment to the customer. However, these designs are typically far from optimal under real-life conditions of machine use such as hard-to-predict and changing disturbance situations, ageing/degradation of machine parts, evolving type of machine usage, and flexible customer specifications.
Therefore, the goal of this Ph.D. project is to develop a data-based performance optimization strategy to make control systems agile under changing circumstances. We envision to develop novel extremum seeking control methods that guarantee optimal performance in terms of time-varying system behavior, while dealing with changing circumstances of use, hard constraints related to hardware and customer specifications, and nonlinear system behavior. Another goal of such data-based strategy may be to achieve uniform performance over an entire machine park. These objectives are envisioned to be achieved by exploiting only measured performance and constraint data and limited prior system knowledge. The fact that such an approach allows for large model uncertainties, being in essence a model-free approach, makes such approach also particularly suitable for complex, multi-physics problems in high-tech systems. As such, it can be used to make system-wide, multi-disciplinary design trade-offs in an online fashion.
Within this PhD project a collaboration with the high-tech semiconductor company ASM PT will be fostered. People involved in supervision:
The starting dates are flexible but before January 2021.
Moreover, the project will offer to the students an extensive training program on Systems and Control in the scope of the Dutch Institute for Systems and Control (http://disc.tudelft.nl/). Moreover, a training program focusing on more generic and transferable skills required by professional researchers is offered. This provides the students with a solid background for their research and future careers.
The candidate should have
Interviews with the selected PhD-candidates will take place on-site at TU/e in the Netherlands (if restrictions associated to the Covid-19 situation permit).
Do you recognize yourself in this profile and would you like to know more?
Please contact prof.dr.ir. Nathan van de Wouw (n.v.d.wouw[at]tue.nl)
More information about terms of employment can be found here.
Please visit www.tue.nl/jobs to find out more about working at TU/e!
We invite you to submit a complete application by using the 'solliciteer nu'-button on this page. The application should include a:
We look forward to your application and will screen it as soon as we have received it. Screening will continue until the position has been filled.
We do not respond to applications that are sent to us in a different way. Please keep in mind you can upload only 5 documents up to 2 MB each. If necessary please combine files.