KU Leuven

Engineering-insight-inspired Data-driven Models for Machine Digital Twins

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KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.

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(ref. BAP-2021-871)

Last modification : Tuesday, December 14, 2021

The research is hosted by the KU Leuven Noise and Vibration Research Group – as part of the Mecha(tro)nic System Dynamics division (LMSD), which currently counts >100. This research track is supervised by prof. Frank Naets (https://www.kuleuven.be/wieiswie/en/person/00055809 ) and prof. Elke Deckers (https://www.kuleuven.be/wieiswie/en/person/00059933 ). The research group has a long track record of combining excellent fundamental academic research with industrially relevant applications, leading to dissemination in both highly ranked academic journals as well as on industrial fora. More information on the research group can be found on the website: https://www.mech.kuleuven.be/en/research/mod/about and our linkedIn page: https://www.linkedin.com/showcase/noise-&-vibration-research-group.

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The KU Leuven Mecha(tro)nic System Dynamics division (LMSD) is searching for a research engineer to join its team to work in the challenging E2Comation project: Life-cycle optimization of industrial energy efficiency by a distributed control and decision-making automation platform.


  • This PhD is part of a project aiming to develop a novel approach towards an integrated production paradigm based on energy efficiency and sustainable management. There must be an effort in industry to reduce the consumption and the request of energy. However, in order to optimize the available resources in a production environment, an effective digital twin needs to be developed to assess the trade-offs associated with various machine and process settings. In this project a fully integrated framework will be developed which allows to go from data-capture in the industrial operation to definition of the digital twin and it’s exploitation for improving the overall performance of the different available assets. In this PhD, the focus is on the development of the digital twin in this framework.
  • As a researcher you will investigate novel methods to combine the available measurements on existing industrial machines (machine settings, temperatures, power consumption, …) with a-priori engineering knowledge (general trending rules like power consumption as a function of velocity) into a predictive digital twin. In this framework you will employ various machine learning methods, evolving from classical neural networks to recurrent neural networks with various structures and types of regularization. You will develop and validate these approaches on an in-house dataset from an injection-moulding process, and on industrial processes and datasets provided in the project.


If you recognize yourself in the story below, then you have the profile that fits the project and the research group.

  • I have a master degree in engineering, physics or mathematics and performed above average in comparison to my peers.
  • I am proficient in written and spoken English.
  • During my courses or prior professional activities, I have gathered some basic experience with numerical modelling methods and/or data analysis. Prior experience with machine learning is plus.
  • As a PhD researcher of the KU Leuven Noise and Vibration Research Group I perform research in a structured and scientifically sound manner. I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
  • Based on interactions and discussions with my supervisors and the colleagues in my team, I set up and update a plan of approach for the upcoming 1 to 3 months to work towards my research goals. I work with a sufficient degree of independence to follow my plan and achieve the goals. I indicate timely when deviations of the plan are required, if goals cannot be met or if I want to discuss intermediate results or issues.
  • In frequent reporting, varying between weekly to monthly, I show the results that I have obtained and I give a well-founded interpretation of those results. I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
  • I feel comfortable to work as a team member and I am eager to share my results to inspire and being inspired by my colleagues.
  • I value being part of a large research group which is well connected to the machine and transportation industry and I am eager to learn how academic research can be linked to industrial innovation roadmaps.
  • During my PhD I want to grow towards following up the project that I am involved in and representing the research group on project meetings or conferences. I see these events as an occasion to disseminate my work to an audience of international experts and research colleagues, and to learn about the larger context of my research and the research project.


  • A remuneration package competitive with industry standards in Belgium, a country with a high quality of life and excellent health care system.
  • An opportunity to pursue a PhD in Mechanical Engineering, typically a 4 year trajectory, in a stimulating and ambitious research environment.
  • Ample occasions to develop yourself in a scientific and/or an industrial direction. Besides opportunities offered by the research group, further doctoral training for PhD candidates is provided in the framework of the KU Leuven Arenberg Doctoral School (https://set.kuleuven.be/phd), known for its strong focus on both future scientists and scientifically trained professionals who will valorise their doctoral expertise and competences in a non-academic context. More information on the training opportunities can be found on the following link: https://set.kuleuven.be/phd/dopl/whytraining.
  • A stay in a vibrant environment in the hearth of Europe. The university is located in Leuven, a town of approximately 100000 inhabitants, located close to Brussels (25km), and 20 minutes by train from Brussels International Airport. This strategic positioning and the strong presence of the university, international research centers, and industry, lead to a safe town with high quality of life, welcome to non-Dutch speaking people and with ample opportunities for social and sport activities. The mixture of cultures and research fields are some of the ingredients making the university of Leuven the most innovative university in Europe (https://nieuws.kuleuven.be/en/content/2018/ku-leuven-once-again-tops-reuters-ranking-of-europes-most-innovative-universities). Further information can be found on the website of the university: https://www.kuleuven.be/english/living


For more information please contact Prof. dr. Frank Naets, tel.: +32 16 37 26 93, mail: frank.naets@kuleuven.be.

You can apply for this job no later than January 31, 2022 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.

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Engineering-insight-inspired Data-driven Models for Machine Digital Twins
Oude Markt 13 Leuven, België
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KU Leuven is an autonomous university. It was founded in 1425. It was born of and has grown within the Catholic tradition.

De pagina van de werkgever bekijken

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