13 July 2020

Apply before 30 September 2020

The Robotics, Automation and Mechatronics (RAM) division of the Department of Mechanical Engineering is looking for a doctoral researcher to join the MECO research team.


Collaborative robots or “cobots” are robots designed to operate in direct physical contact with a human operator. Cobots play a key role in the realization of the Industry 4.0 transformation towards efficient and agile flexible manufacturing processes. Current state-of-the-art cobots have however a limited usability because of their low payload capacity imposed by safety. This is a direct result of their traditional design with traditional actuation, limiting their payload-to-mass ratio of about 1:10.

In the Flemish ELYSA (Energy-efficient, Lightweight, safe Yet strong manipulator Arm for cobot applications) project, KU Leuven and the University of Brussels (VUB) join forces to design and develop a new generation of cobots, aiming at a payload-to-mass ratio of 1:1. The key elements of this project are a new actuator concept and a matching control framework that can cope with the complex and payload dependent nonlinear actuator characteristics.

In this research you will contribute to the development, implementation and experimental validation of the novel control framework. This framework will take into account the payload dependent dynamic model of the robot system, including its flexibilities, in order to avoid or compensate vibrations resulting from the latter. Your focus will be on the development, implementation and experimentally validation of new off-line and on-line data-driven modelling techniques: optimization based combined model structure selection, parameter and state estimation. You will research regularized estimation to find trade-offs between model complexity and accuracy, estimate model uncertainty to account for in the robust control design. You will first work on numerical models of the novel cobot, and gradually, as the hardware becomes available move towards experimental application and validation. You will closely cooperate with the other PhD researchers involved in this project and that focus on the mechanical design and controller development.

You will be embedded in the MECO (Motion Estimation Control and Optimization) research team of the KU Leuven Department Mechanical Engineering. The MECO team focusses on the identification, analysis and control of mechatronic systems such as autonomous guided vehicles, robots, and machine tools. It combines theoretical innovations with experimental validations. The theoretical research benefits from the team’s expertise on numerical optimization, while MECO’s practical knowhow and industrial collaboration are supported by its participation in Flanders Make – the Flemish strategic research center for the manufacturing industry.


Ideal candidates hold a Master’s degree in engineering, computer science, or applied mathematics. Successful candidates are typically ranked at or near the top of their classes, have a solid background in systems and control, robotics, optimization, relevant computer programming skills (Python or Matlab, C++), and enthusiasm for scientific research, including real-hardware implementations and experiments. Team player mentality, independence, and problem solving attitude are expected, and proficiency in English is a requirement.

Applicants whose mother tongue is neither Dutch nor English must present an official language test report. The acceptable tests are TOEFL, IELTS, and Cambridge Certificate in Advanced English (CAE) or Cambridge Certificate of Proficiency in English (CPE). Required minimum scores are:

  • TOEFL: 600 (paper-based test), 100 (internet-based test);
  • IELTS: 7 (only Academic IELTS test accepted).

A fully funded PhD position in an international context for four years at KU Leuven: a top European university and a hub for interdisciplinary research in the fields of systems, control and optimization. The doctoral candidate will work in world-class facilities with highly qualified experts, and will benefit from the training scheme developed based on the expertise of academic and industrial partners. A start date in the course of 2020 or early 2021 is to be agreed upon.