PhD Research Positions in Hybrid Modelling and Virtual Commissioning of Industrial Machines with the Operator in the Loop

Ghent University

Belgium

PhD research positions

Hybrid modelling and virtual commissioning of industrial machines with the operator in the loop

 

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 (www.ugent.be/ea/emsme/en). 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 (visit.gent.be/en/HOME). Our research group is also associated to Flanders Make (www.flandersmake.be/en), 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 on REXPEK, a Strategic Basic Research project funded by Flanders Make. The project resides in the virtual commissioning paradigm where (near) first-time-right commissioning of machines is pursued by means of model based design approaches. The key idea is to determine optimal machine settings through virtual experiments. For this concept to work, the reality gap between the virtual and physical experiments needs to be minimized. Further, in order to reduce the remaining physical experiments, each attempt must be chosen adequately so to yield as much information as possible. The project takes a unique angle to this problem by taking into account the human as a valuable resource both as a knowledge base, a performance sensor or critique as well as a discriminator between what could be good or bad settings. The project has the overall aim to develop new methodologies to assist operators with machine commissioning and real-time tuning.

We are looking for two outstanding PhD candidates that each focus on one of the two following research paths:

-          Specific research challenge on developing hybrid modelling methods that combine physics based expert models with historical and on-site data whilst striving towards real-time model evaluation and model improvement. You will pursue a probabilistic modelling approach taking into account unobservable context variables and will work towards iterative sampling schemes to identify actual context probabilities on the basis of real-world physical experiments.

-          Specific research challenge on developing new Bayesian Optimization (BO) techniques. BO is a black-box optimization strategy that has proven to be valuable in the face of physical experimentation. You will pursue BO methods that can exploit a model based prior whilst simultaneously identifying a context probability that cannot be observed directly.

In the past years our research group has accumulated critical expertise and laboratory infrastructure to support this PhD.

The candidate is expected to

  • Perform high quality and cutting edge research and strive towards successful project execution.
  • Develop machine learning methodologies and software (Python) for probabilistic modelling, identification and decision-making.
  • 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 in machine learning, (probabilistic) modelling, system identification and numerical optimization.
  • Starting date 01/10/2022 (at the earliest, but flexible).

Our offer

Your profile

We are looking for a team member with a background in (probabilistic) machine learning, physics based modelling and numerical simulation of mechatronic applications and some experience with optimization methods. You are quick-witted, have an appetite for the theoretical and are keen on applying and/or improving your programming skills.

  • You hold a M.Sc. in (electro-)mechanical engineering or related engineering fields such as control & automation.
  • You have proven experience with modelling and simulating of mechanical, electromechanical, mechatronic & robotic systems.
  • 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. 

Interested?

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


In your application, please refer to Polytechnicpositions.com

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