PhD Research Position in Sample Efficient Control and Planning through Control as Inference

Ghent University


PhD research position

Sample Efficient Control and Planning through Control as Inference


Research Group active on modelling, control and optimization for Mechatronic systems, Industrial robots and machines, within the Faculty of Engineering, Ghent University, is looking for outstanding applicants for a doctoral position.

About us

Our research group works on modelling, control and optimization for Mechatronic and electromechanical systems, Industrial robots, and processes. We are part of the department of Electromechanical, Systems and Metal Engineering within the Faculty of Engineering of Ghent University ( Ghent University is a top 100 university worldwide and one of the major universities in Belgium, with more than 44000 students and 15000 staff members. Our campus is situated in Ghent, a lively city at the heart of Europe ( Our research group is also associated to Flanders Make (, the network for the Flemish manufacturing industry that helps to develop and optimize products and production processes based on high-tech research. The candidate will be directly embedded in an international research group, working together as a team and will have the possibility to collaborate with many other people active at Ghent University and within Flanders.

Job description

You will work in the context of CTRLxAI, a Strategic Basic Research project funded by the Flemish Foundation for Scientific Research ( Over the past decades the control and Reinforcement Learning (RL) community have produced vastly different solutions to what is essentially the same problem. The project wants to identify and leverage similarities between classical (optimal) control theory and RL towards the development of a new family of adaptive control algorithms. The anticipated outcome of the project are a set of algorithms that (1) learn & explore safely, (2) are (highly) sample efficient, (3) remain stable & robust, and, finally (4) whose decision making can be reverse engineered to some human centric principles and that are therefore explainable. The goal is to be able to handle non-repetitive tasks autonomously and under changing conditions in a wide variety of industrial environments & settings.

Your personal research will focus on theory and algorithms that overarch nonlinear optimal control and RL. Your research will include two main directives. First you will focus on rephrasing the generic stochastic optimal control problem as a probabilistic inference problem. This concept is referred to as Control as Inference (CaI). Based on the recasted problem, a set of novel algorithms will be set up tailored to efficient trajectory optimization. Based on the gained expertise with this concept, you will extend the framework to the concept of active learning (AL). AL methods actively balance the exploration and exploitation incentive to any learning method. You will extend this framework to control applications.

The candidate will be expected to

  • Perform high quality and cutting edge research and strive towards successful project execution.
  • Develop machine learning methodologies and software (Python) for control.
  • Present research at conferences and in journals. 
  • Cooperate with researchers active within the research group and outside.
  • Contribute to the teaching related to modelling and optimization.
  • A 4 years period doctoral position.
  • An internationally competitive salary that corresponds to the salary scales for Doctoral Research Fellows as established by the Flemish government.
  • Access to state-of-the-art tools and facilities, a network of Flemish companies active in the manufacturing industry, and the possibility to collaborate with other research groups.
  • The time to apply and improve your knowledge and skills on state-of-the-art control & machine learning
  • Starting date: 01/09/2022 (but can be flexible).

Our offer

Your profile

We are looking for a team member with a background in control theory, specifically in optimal control and (Bayesian) estimation, numerical optimization methods and (probabilistic) machine learning. You are quick-witted, have an appetite for the theoretical and are keen on applying and/or improving your programming skills towards real-world applications.

  • You hold a M.Sc. in electromechanical engineering or related engineering fields such as control & automation.
  • You have proven experience with numerical optimization methods in machine learning, system design and/or trajectory optimization.
  • You have proven experience in Python.
  • You have experience in or understanding of artificial intelligence and (probabilistic) machine learning methods.
  • You have a team player mindset, a strong personality and work in a result-oriented manner.
  • You are creative and willing to work in a multidisciplinary context.
  • You are proficient in oral and written English and have strong communication skills.
  • You are willing to extend your network and able to talk on technical matters. 


Send your CV, containing 1 or more references and a motivation letter to dr. Tom Lefebvre ( and dr. Saeideh Khatiry Goharood ( including ‘CTRLxAI CAI PHD’ in the email subject before Friday 31/07/2022. If you pass the pre-selection, you will receive further instructions on the selection process and will be invited for a (online) job interview.

In your application, please refer to