Job Description
We are seeking highly motivated postdoctoral researchers to contribute to a cutting-edge research effort focused on lifelong learning and adaptation using digital twins for continuous process optimization in industrial processes.
* Lifelong learning and adaptation will be achieved through the application of methods such as reinforcement learning, continual learning, Bayesian optimization, and adaptive control.
* Digital twin-based learning and optimization methods will be developed for manufacturing processes like 3D Printing, laser cutting, precision motion, and robotic manipulation, utilizing machine learning, federated learning, and optimization techniques.
The results of this research will be demonstrated on real-world advanced manufacturing and robotic systems in collaboration with industrial partners, enhancing the efficiency and sustainability of their products.
As part of our interdisciplinary team, you will have the opportunity to translate your own research ideas into practical solutions for these challenges. Your responsibilities will include supporting our master students, publishing research papers, contributing to teaching efforts, and participating in conferences.
This position is supported by the NCCR Automation and the European Project DMaaST, offering excellent opportunities for national and international collaboration with academic and industrial partners.
Your Profile
To be successful in this role, you should have a doctoral degree in electrical, mechanical, or industrial engineering and possess significant research experience in manufacturing processes and automation solutions. Proficiency in Python programming, modeling, and data analysis will enable you to contribute to our ongoing software development efforts. Excellent spoken and written English skills are essential for navigating our international environment.