-
EA0074: Internship - Control Policy Learning with Guarantee
MERL is seeking a highly motivated and qualified individual to conduct research in the integration of model- and learning-based control to achieve high precision positioning with guaranteed safety and robustness. The ideal candidate should have solid backgrounds in dynamical systems, control theory and state-of-the-art control policy learning algorithms, and strong coding skills. Prior experience on ultra-high precision motion control systems is a plus. Ph.D. students in learning and control are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
- Research Areas: Control, Machine Learning, Dynamical Systems
- Host: Yebin Wang
- Apply Now
-
EA0071: Internship - Modeling and Estimation of Electrical Machines
MERL is seeking a highly motivated and qualified individual to conduct research in differentiable modeling, estimation and control of electrical machines. The ideal candidate should have solid backgrounds in dynamical modeling of electrical machines, parameter estimation, and control theory. A proven record of publishing results in leading conferences/journals is necessary. Demonstrated knowledge of sensorless drive and experience of using dSPACE for real-time HIL experimentation is a plus. Senior Ph.D. students in electrical engineering, control, and related areas are encouraged to apply. Start date for this internship is flexible and the duration is about 3 months.
- Research Areas: Electric Systems, Control, Dynamical Systems
- Host: Yebin Wang
- Apply Now