Required Qualifications
● Strong fundamentals in classical control theory (PID, impedance/admittance control, state estimation) with hands-on experience implementing controllers on physical hardware.
● Proficiency in Python and C/C++ for real-time embedded or robotic applications.
● Hands-on experience with inverse kinematics solvers and simulation environments (PyBullet, MuJoCo, Drake, or similar), including practical familiarity with IK libraries or custom Jacobian-based implementations.
● Experience with rigid-body kinematics, coordinate frame transformations (homogeneous transforms, SE(3), quaternion representations)
● Experience with sensor fusion techniques (Kalman filtering, complementary filters) using IMU, encoders, or similar proprioceptive sensors.