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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 Quasimo strategic basic research project.

The research is hosted by the KU Leuven Mecha(tro)nic System Dynamics division (LMSD), which currently counts >100 researchers. This research track is supervised by prof. Frank Naets (https://www.kuleuven.be/wieisw... ). 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/e... and our linkedIn page: https://www.linkedin.com/showcase/lmsd-kuleuven/.

Website unit https://www.mech.kuleuven.be/e...

  • This PhD is part of a project aiming to develop a novel approach for Bayesian-network-based performance analysis of mechatronic systems. A fundamental challenge in these frameworks is the quality of the data that is fed into the Bayesian network decision network, as these schemes are typically not suitable for handling the high sampling-rate data captured in mechatronic applications. Therefore, there is a need to develop a framework which allows to convert the bulk high frequency data which is generated into lower frequency information which can be effectively consumed by the Bayesian network, or which can be exploited directly towards condition monitoring applications.
  • As a researcher in this project you will investigate novel methods to identify equivalent models of various hard-to-model effects in mechatronics systems in real-time, and to convert these identified models into relevant information. The framework which will be (initially) investigated relies on disturbance estimation methods which are exploited to convert the time-dependent measurement data into a state-dependent feature space with representative physical properties. Machine learning methods (regressors and qualifiers) will be exploited alongside conventional parameter identification to convert this feature space into representative physical quantities. You will validate the developments in first instance on academic problems, and finally scale them to (close-to-)industrial complexity cases.

To apply for this position, please follow the application tool and enclose:

1. full CV – mandatory

2. motivation letter – mandatory

3. full list of credits and grades of both BSc and MSc degrees (as well as their transcription to English if possible) – mandatory (when you haven’t finished your degree yet, just provide us with the partial list of already available credits and grades)

4. proof of English proficiency (TOEFL, IELTS, …) - if available

5. two reference letters - if available

6. an English version of MSc or PhD thesis, or of a recent publication or assignment - if available

For more information please contact prof. Frank Naets (frank.naets@kuleuven.be) by mail and mention [Quasimo Vacancy] in the title.
You can apply for this job no later than June 30, 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.




Apply before: 30/06/2022

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 state-estimation and parameter identification (optimization) methods.
  • I have some background in mechatronic modelling and 1D simulation frameworks like Simulink/Simscape.
  • I have some background and/or an interest to grow my knowledge in machine learning methods.
  • I am proficient in programming basic methods in Matlab and/or Python.
  • 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/do...;
  • 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/englis...

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