Job description
We are looking for motivated postdoctoral researchers to contribute to this effort. The envisioned research will address :
Lifelong learning and adaptation using digital twins for continuous process optimization in industrial processes, using methods such as reinforcement learning, continual learning, Bayesian optimizationand adaptive control.
Development of digital twin-based learning and optimization methods for manufacturing processes such as 3D Printing, laser cutting, precision motion, and robotic manipulation using methods in machine learning, federated learning and optimization.
For both poistions, the results will be demonstrated on real-world advanced manufacturing and robotic systems in collaboration with industrial partners, helping to improve the efficiency and sustainability of their products.
Your goal will be to translate your own research ideas to tackle these challenges, in close collaboration with our interdisciplinary team. As part of this process, you will support our master students, publish in scientific journals, contribute to our teaching efforts, and participate in conferences. The positions are supported by the NCCR Automation and the European Project DMaaST that offer excellent opportunities for national and international collaboration with academic and industrial partners.
Your profile
You are highly motivated and dedicated with a doctoral degree in electrical, mechanical or industrial engineering. You are experienced as a researcher with an active interest in manufacturing processes and in developing automation solutions to improve their efficiency and sustainability. Programming, modelling, and data analysis skills in Python and support you in contributing to our ongoing software development efforts. Your spoken and written English skills help you navigate our international environment.
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