Please use the online application tool to submit your application and include:
• an academic CV with photo,
• a Pdf of your diplomas and transcript of course work and grades,
• statement of research interests and career goals (max. 2 pages),
• sample of technical writing (publication or thesis),
• contact details of at least two referees,
• proof of English language proficiency test results.
For more information, send an e-mail to firstname.lastname@example.org. Subject of your email should be: “Multi-systems learning PhD application”.
You can apply for this job no later than December 31, 2018 via the online application tool
KU Leuven seeks to foster an environment where all talents can flourish, regardless of gender, age, cultural background, nationality or impairments. If you have any questions relating to accessibility or support, please contact us at diversiteit.HR@kuleuven.be.
You will be embedded in the MECO (Motion Estimation Control and Optimization) research team of the KU Leuven Department Mechanical Engineering. The MECO research team focusses on identification, analysis and optimal control of mechatronic systems such as autonomous guided vehicles, robots, and machine tools. It combines theoretical contributions (development of design methodologies) with experimental knowhow (implementation and experimental validation on lab-scale as well as industrial setups). The theoretical research benefits from the group’s expertise on numerical optimization, especially convex optimization. MECO is member of Flanders Make - the strategic research centre for the manufacturing industry.
You will develop multi-system learning techniques for interconnected mechatronic systems. Learning techniques are used to improve the performance of systems that execute the same or similar operations over and over again. This performance improvement is realized gradually by exploiting the repetitive nature of these operations. Learning techniques are already finding their way to single systems in the mechatronic industry. However, they often suffer from long convergence periods and non-monotonic improving behavior. The new trend of interconnecting mechatronic systems (directly or through the cloud) offers new ways to improve these learning algorithms: instead of learning per machine, learning can be done for multiple systems in parallel by sharing information, resulting in an overall learning algorithm which is more efficient (shorter convergence periods) and more effective (a better performance for all systems). The project will involve theoretical innovations as well as implementations of the developed learning techniques and experimental validations. Several experimental demonstration cases are available for this research, e.g. three bar-linkage setups, a set of twenty similar mobile platforms, ….
Ideal candidates hold a Master’s degree in engineering (mechanical, control ...). Successful candidates have typically ranked at or near the top of their classes, have a solid background in systems, control, and numerical optimization, relevant computer programming skills, a strong interest and experience in real-world applications, and enthusiasm for scientific research. 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:
A fully funded PhD position in an international context for four years at the KU Leuven: a top European university and a hub for interdisciplinary research in the fields of systems, control and optimization. You will be embedded in the MECO research team of the Department of Mechanical Engineering. 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 2018 is to be agreed upon.