Job Summary
We are seeking highly motivated doctoral students to contribute to our research effort. The envisioned research will address various aspects of advanced manufacturing systems, including online learning-based control and control-oriented machine learning.
Key Responsibilities:
* Develop and implement online learning-based control algorithms for advanced manufacturing systems using Reinforcement Learning, Online Convex Optimization, and Iterative Learning Control.
* Optimize system-level hierarchical controllers for industrial systems and decision-making using bilevel, on-line, and differentiable optimization methods.
* Design and develop control and task planning strategies for advanced manufacturing using domain knowledge and expert feedback from domain adaptation, transfer learning, and large-language models.
* Explore digital twin-based learning and optimization for manufacturing processes such as 3D Printing, laser cutting, precision motion, and robotic manipulation using machine learning, federated learning, and optimization techniques.
The results will be demonstrated on real-world advanced manufacturing and robotic systems in collaboration with industrial partners, aiming to improve efficiency and sustainability.
Your Profile:
You should have a master's degree in electrical, mechanical, or industrial engineering, with strong programming, modelling, and data analysis skills in Python and machine learning/optimization libraries. Proficiency in spoken and written English is essential for navigating our international environment.
About the Opportunity:
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.