Job Responsibilities
1. Develop deep reinforcement learning control and planning decision algorithms for systems such as drones and multi-degree-of-freedom robots;
2. Conduct research on cutting-edge reinforcement learning algorithms and optimize them, leading data construction, model training, and debugging/validation;
3. Collaborate closely with project teams to drive the practical implementation of reinforcement learning algorithms in real-world systems like drones and robots.
Job Requirements
1. Master's degree or higher in Computer Science, Electronic Engineering, Applied Mathematics, Automation, or related fields; PhD holders with publications in top conferences/journals preferred;
2. Familiarity with autonomous robot system architecture, proficiency in robotics software stacks such as ROS, and fluent use of C++ or Python;
3. Familiarity with mainstream robotics simulation platforms such as MuJoCo, V-REP, Gazebo, and NVIDIA Isaac;
4. Proficiency in mainstream deep reinforcement learning algorithms (e.g., PPO, DDPG, A3C) and understanding of fundamental computer vision methods;
5. Preference given to candidates with research or engineering experience applying reinforcement learning algorithms to robotic tasks.
