7 months ago
Zürich, SwitzerlandIntern
Responsibilities
- Train policies for navigation and avoidance in complex 3D environments.
- Fuse learned cost shaping with trajectory optimization for agile flight.
- Build scalable datasets and training loops for real-world validation.
- Develop assistive policies for human-in-the-loop shared control.
- Explore decentralized coordination for multi-agent operations.
Requirements
- PhD student in Robotics, Machine Learning, Controls, or related field.
- Strong fundamentals in reinforcement learning and control theory.
- Proficient in Python and at least one of C++ or CUDA.
- Hands-on experience with robotics simulation tools.
- Experience in training policies for navigation or manipulation on real robots.
Benefits
- Real hardware testing with production-ready aircraft.
- Cross-disciplinary mentorship opportunities.