Doctoral fellow

Job description

HAVE YOU TRIED THIS? BAYESIAN OPTIMIZATION FOR VIRTUAL TESTING AND COMMISSIONING

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 objective is to develop tools to suggest optimal machine settings through virtual and on site experimentation. Therefore, the reality gap between the virtual and physical experiments need be minimized. In order to reduce the remaining physical experiments, each attempt must be chosen 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.

Your specific research challenge lies in the use of existing and development of new Bayesian Optimization (BO) techniques. BO is a black-box optimization strategy that operates on function evaluations alone originally developed to optimize functions that could be evaluated only through physical experimentation. You will pursue application of these methods that can exploit a model based prior whilst simultaneously identifying a context probability that can(not) be observed. In the past years our research group has accumulated critical expertise and laboratory infrastructure to support this PhD.

We offer a full-time position as a doctoral fellow, consisting of an initial period of 12 months, which - after a positive evaluation, will be extended to a total maximum of 48 months.

The candidate will be expected to

• Perform described research and strive towards successful project execution.
• Develop software tools (Python) for probabilistic optimization & identification.
• Present research at conferences and in journals.
• Cooperate with researchers active within the research group and outside.
• Contribute to the teaching related to modelling, control and optimization.

Job profile

A background in modelling, control & numerical optimization methods and experience with system identification concepts is an asset. As a person you are quick-witted, learn fast and program faster. There are many ideas pending implementation and many more to explore. You feel at ease doing either.

Hard skills

• 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 system design (and machine learning).
• You have experience in probabilistic methods (and machine learning).
• You have proven experience in Python.
  
  

Soft skills

• 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.

How to apply

Send your CV, containing 1 or more references and a brief motivation letter to dr. Tom Lefebvre (Tom.Lefebvre@ugent.be) including ‘REXPEK PHD’ in the email subject before Wednesday 31/01/2024. If you pass the pre-selection, you will receive further instructions on the selection process and will be invited for an online job interview.