The TU/e offers a PhD position within the 'Learning in Motion' research project. This project is a collaboration between the TU/e and the companies Canon, ASM PT and Thermo Fisher Scientific, developing respectively, industrial printers, back-end semi-conductor equipment and electron microscopes. The project involves four PhD positions, of which this vacancy is one.

The PhD position is embedded within the Dynamics and Control section of the Mechanical Engineering Department of TU/e. The Dynamics and Control section is home to 13 faculty members and approx. 40 researchers (PhD and postdoc) in the field of dynamical systems and control and offers a vibrant environment with excellent lab facilities on high-tech systems, robotics and automotive systems.

Background
High-tech motion systems are becoming increasingly complex to meet ever increasing market demands on quality and versatility of system performance. Such design complexity puts under pressure a purely model-based engineering approach towards motion system design in all its aspects.

Project objective
The growing complexity in designing high-tech motion systems urges for a design approach that complements model-based design with state-of-the-art data-based learning techniques. Therefore, the overarching objective of the 'Learning in Motion' project is to develop a holistic approach towards using data-driven learning techniques for high-performance motion systems, in the sense that data-driven techniques will be used to improve and/or realise uniform performance of all components relevant in a motion system design.

The PhD project associated to this vacancy aims to develop digital twins for web and belt handling in industrial printing systems, in collaboration with the company Canon Production Printing, and addresses the following sub-goals:

1. to develop a hybrid physics-based and data-based modelling approach towards digital twinning;
2. to develop methods for complexity management for such hybrid digital twins,

both in the scope of the above high-tech printing application domain.

People involved in supervision:

• Prof.dr.ir. Nathan van de Wouw (www.vandewouw.dc.tue.nl)
• Prof.dr.ir. T.A.E. Oomen (www.toomen.eu)

The starting dates are flexible but preferably before January 2023.

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 (https://www.tue.nl/en/working-at-tue/phdpdeng/professional-development/). This provides the students with a solid background for their research and future careers.

The PhD candidate should have

• a MSc degree in Mechanical Engineering, Electrical Engineering or Systems and Control with a solid background in dynamics and control, and some experience in machine learning is considered an advantage.
• a strong interest and skills in both 1) developing new fundamental theories for hybrid digital twinning and model complexity reduction and 2) applying such novel scientific developments to industrial high-tech context of Canon.
• Excellent communication skills and written/verbal knowledge of the English language.

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)

A meaningful job in a dynamic and ambitious university, in an interdisciplinary setting and within an international network. You will work on a beautiful, green campus within walking distance of the central train station. In addition, we offer you:

• Full-time employment for four years, with an intermediate evaluation (go/no-go) after nine months. You will spend 10% of your employment on teaching tasks.
• Salary and benefits (such as a pension scheme, paid pregnancy and maternity leave, partially paid parental leave) in accordance with the Collective Labour Agreement for Dutch Universities.
• A year-end bonus of 8.3% and annual vacation pay of 8%.
• High-quality training programs and other support to grow into a self-aware, autonomous scientific researcher. At TU/e we challenge you to take charge of your own learning process.
• An excellent technical infrastructure, on-campus children's day care and sports facilities.
• An allowance for commuting, working from home and internet costs.
• A Staff Immigration Team and a tax compensation scheme (the 30% facility) for international candidates.