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.
Deadline:November 30, 2018!
For more information, send an e-mail to email@example.com. Subject of your email should be: “MPC PhD application".
You can apply for this job no later than November 30, 2018 via the online application tool.
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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 on lab-scale as well as industrial setups. 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 - a strategic research center for the manufacturing industry.
You will develop cutting-edge optimization algorithms tailored to MPC of mechatronic systems such as robots, electric cars, and CNC production machines.The successful application of MPC to these system hinges upon an effective problem formulation combined with an efficient and robust optimization algorithm. To meet these requirements you will combine and extend recent developments within the MECO research team on spline-based motion planning and numerical optimal control. MECO’s current optimal motion planning research exploits the properties of B-splines to transform the problem into a small-scale nonlinear programming problem (check out https://github.com/meco-group/... to get a flavor). Under the hood, CasADi (www.casadi.org) is used as symbolic optimization framework and algorithmic differentiation tool. Through theoretical and methodological innovations you will extend these initial results to general and efficient spline-based MPC approaches, and you will build effective tools for applications such as motion planning for automated guided vehicles on top of these approaches. In addition, efficient numerical implementations of the algorithms in C/C++ are envisaged as well as experimental validations on mechatronic systems.
Ideal candidates hold a Master’s degree in engineering, computer science, or applied mathematics. Successful candidates have typically ranked at or near the top of their classes, have a solid background in optimization and/or control, relevant computer programming skills (Python or Matlab, C++), 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 KULeuven: 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 or the first semester of 2019 is to be agreed upon.