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.
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.
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.
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.
Prior experience with machine learning is plus.
I read technical papers, understand the nuances between different theories and implement and improve methodologies myself.
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.
I iterate on my work and my approach based on the feedback of my supervisors which steer the direction of my research.
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.
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.
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