Data-Driven Modelling Methods for Digital Twins of Circular Production Systems

Il y a 3 mois


Leuven, Belgique KU Leuven Temps plein
You hold a Master’s degree in Science, Engineering, or an equivalent degree which you have obtained with a high GPA.You enjoy working in a multidisciplinary team of international researchers and show willingness to learn and explore innovative technologies and techniques.You have a creative mindset, enjoy taking initiative, and engage in exploring innovative ideas and new opportunities.You are fluent in English.You are member of a multidisciplinary research group working on the topic of Digital Twins for Circular Production Systems at KU Leuven under the supervision of Prof. Giovanni Lugaresi.You study and critically evaluate existing literature, patents, company releases, and use the obtained information to determine the state of the art and to identify opportunities for novel contributions.You are actively engaged in the development of innovative methods and algorithms to be integrated in a digital twin architecture for manufacturing systems.You contribute to organizing and performing testing and validation experiments within the lab setups available at the laboratories of KU Leuven.You disseminate your research results in the occasion of project meetings and at international conferences.You publish in high-quality international scientific journals.The KU Leuven research group within the Department of Mechanical Engineering has significant experience in manufacturing systems. The main industrial applications are discrete manufacturing processes such as assembly lines, as well as de- and re-manufacturing processes. The newly established team in KU Leuven has developed experience in digital twins and applied it within various research activities and international projects. The group’s ambition is to enable next-generation digital twins to become effective decision-support systems within production environments, reaching a higher integration with existing software components, databases, as well as control systems. This is particularly challenging for new production paradigms such as demanufacturing, where the input flow variability and the frequent reconfigurations make traditional planning methods weak. Hence, in this project different methods will be explored to further extend critical functionalities of simulation-based digital twins (i.e. discrete event simulation). Among others, research will be focused on methods for data-driven model generation, model validation, and physical-to-digital alignment. The developed methods will be tested in experiments within both digital and realistic prototypical environments.We offer a varied and challenging job in close cooperation with industry, combined with an attractive salary package.We offer a fully-funded research position in a stimulating and multi-disciplinary environment and a strong industrial network.We provide the opportunity to work towards obtaining a PhD degree and/or to grow your academic experience and track record within an international network.

Expected start date: January-February 2025.



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