Reinforcement Learning Intern

📋 Description

• Collect tele-operated simulation and/or real data from the surgical robot while performing a
• Train off-the-shelf reinforcement learning algorithms (e.g., policy gradient methods, actor-critic
• Adapt reinforcement learning algorithms to the surgical domain and exercise of interest
• Recommend a suggested algorithm/experimental setup moving forward based on experimental results
• Deploy chosen RL algorithm either in simulation or on the dV5 to validate performance in real-time

🎯 Requirements

• Current enrollment in an Engineering (e.g., Biomedical, Electrical), Robotics, Computer Science, or
• Must be available to work full-time (approximately 40 hours per week) during a 10-12 week period
• Must be currently enrolled in and returning to an accredited degree-seeking academic program after
• Deep experience with a deep learning framework (e.g., PyTorch or JAX) (preferred).
• Strong familiarity with reinforcement learning algorithms (e.g., policy gradient, actor-critic
• Willingness to learn about robotics and application of RL to surgical robotics (preferred).

🎁 Benefits

• Market-competitive compensation packages inclusive of base pay (paid at an hourly rate), benefits