The position focuses on advancing iterative learning control (ILC) and parameter optimization for industrial use, transitioning from theoretical work to practical implementation and testing on physical systems. The role also entails designing adaptive control and estimation methods to facilitate hardware deployment, while working closely with an industry partner to address specific challenges.
Responsibilities
- Design tailored ILC and parameter optimization algorithms for industrial use, progressing from theoretical analysis to implementation and testing on real systems
- Create adaptive control and estimation methods to enable hardware implementation
- Work closely with an industry partner to address unique challenges
- Supervise and support student projects
Requirements
- PhD in robotics, electrical or mechanical engineering, computer science, or a related discipline
- Strong grasp of learning-based control methods and iterative learning control theory, including applications to industrial systems
- Strong background in linear algebra, numerical methods, optimization, and control theory
- Fluent written and oral English
Nice to have
- Experience with online optimization theory and adaptive control theory
- Practical experience with industrial control systems
- German language skills
- Self-organized, structured approach to work with a solution-oriented mindset
- Strong motivation and eagerness to acquire new skills
- Teamwork, positive attitude, and creativity
Quelle: öffentlich zugängliche Karriereseite des Arbeitgebers. Batchly ist nicht der Arbeitgeber und steht nicht notwendigerweise in einem Vertragsverhältnis mit dem Unternehmen.