Reinforcement Learning (Physical AI) Intern at Cadence · Home Place

Summary: Cadence is a company focused on technology innovation, and they are seeking a passionate intern to join their R&D team. The Reinforcement Learning (Physical AI) Intern will work on developing RL prototypes and collaborating on adaptive systems that improve performance over time. Responsibilities: - Develop RL prototypes using algorithms like Deep Q, PPO, and A3C for control and optimization tasks - Integrate RL into simulation environments and robotics simulators - Collaborate on adaptive systems that learn from feedback to improve performance over time - Support internal presentations and documentation for RL-based automation initiatives Required Qualifications: - Strong foundation in reinforcement learning and robotics control - Ability to develop RL prototypes using algorithms like Deep Q, PPO, and A3C for control and optimization tasks - Experience integrating RL into simulation environments and robotics simulators - Ability to collaborate on adaptive systems that learn from feedback to improve performance over time - Support for internal presentations and documentation for RL-based automation initiatives Preferred Qualifications: - Pursuing a degree in Robotics, AI, Computer Science, or related field - Experience with Python, PyTorch, and RL libraries (e.g., Stable Baselines, RLlib) - Hands-on experience with simulators (e.g., Gazebo, MuJoCo) - Strong math optimization and control theory background Required Skills: Reinforcement Learning, Python, PyTorch Important Skills: Robotics Control, Math Optimization, Control Theory, Simulators