Autonomy Engineer Intern, Reinforcement Learning, 2027 (Zurich)

📋 Description

• Train policies that adapt online to cluttered 3D scenes (forests, bridges, urban canyons)
• Fuse learned cost shaping / value functions with trajectory optimization for smooth, agile flight
• Build scalable datasets and training loops with Isaac Lab, domain randomization, residual learning
• Learn assistive policies that blend pilot intent, autonomy priors, and uncertainty-aware behaviors
• Explore decentralized coordination for coverage, pursuit, and collaborative mapping with minimal

🎯 Requirements

• PhD student in Robotics, Machine Learning, Controls, or related field.
• Strong fundamentals in RL, control theory, and motion planning; comfort with safety/robustness
• Proficient in Python (PyTorch/JAX/Ray RLlib) and at least one of C++ or CUDA.
• Hands-on experience with robotics simulation (Isaac Lab/MuJoCo/PyBullet) and sim2real techniques.
• Experience training/deploying policies for navigation, manipulation, or locomotion on real robots

🎁 Benefits

• Real hardware cadence: Prototype in sim, then flight test on production-ready aircraft and edge
• Safety-first learning: We pair RL with formal constraints, estimation of uncertainty, and
• Cross-disciplinary mentorship: Collaborate with perception, mapping, controls, and product teams to
• Compensation: The hourly rate for this position is $47/hr for Undergrad, $53/hr for Masters, and