Doctoral Researcher (m/f/d) Reinforcement Learning and Optimization for Efficient Path Planning of Parallel Robots | Doktorand*in (m/w/d) Verstärkungslernen und Optimierung für eine effiziente Bahnplanung von parallel

Job Summary

This doctoral position at the Institute for Neuroinformatics focuses on developing innovative methods for efficient path planning for Delta robots. The successful candidate will leverage and advance reinforcement learning and optimization algorithms within simulation environments, with a critical emphasis on transferring these developments to real-world robotic systems. This role involves active participation in a DFG-funded project, collaborating with the Chair of Production Systems at Ruhr-Universität Bochum. It offers a unique opportunity to contribute to cutting-edge research in robotics and machine learning, bridging theoretical advancements with practical applications in a dynamic academic setting. The work will involve developing simulation environments, creating optimized path planning algorithms, and validating their performance on physical robots.