Key Responsibilities:
Design, implement, and optimize algorithms for robots using ROS.
Develop state estimation algorithms to improve the accuracy and stability of robotic systems.
Build and optimize motion planning algorithms for humanoid robots.
Research and apply SLAM methods for localization and mapping in unknown environments.
Develop inverse kinematics, trajectory planning, and force control.
Work with simulation environments such as Gazebo, Mujoco, and IsaacGym to test and evaluate algorithms.
Collaborate with software and hardware teams to deploy algorithms on real-world robotic systems.
Requirements:
Bachelor's, Master's, or Ph.D. in Robotics, Control Engineering - Automation, Mechatronics, Computer Science, or related fields.
Proficient in C++ and Python programming, with experience working with ROS/ROS2.
Strong understanding of SLAM, State Estimation, and Motion Planning.
Solid knowledge of optimization algorithms and robotic system modeling.
Preference for candidates with hands-on experience in real robotic systems or deploying algorithms on physical robots.
Preferred Qualifications:
Experience with simulation platforms such as Gazebo, Mujoco, and IsaacGym.
Knowledge of control and motion planning libraries like MoveIt and OMPL.