VehicleMotionControlAI/MLPlatformDesignEngineer

Solid foundation in classical control methods such as PID, state feedback, and observers, Knowledge of advanced control strategies such as adaptive control, model predictive control, learning-based MPC, and ML/AI-based control approaches, knowledge of state estimation and observer design, Experience with sensor fusion methods, including Kalman filter variations such as EKF, UKF, and particle filters, and understanding of system identification and parameter estimation in dynamic systems, Hands-on experience using Python for data analysis and model development, Experience or a interest in applying machine learning or data-driven modeling to control, estimation, and system dynamics problems, Experience with model-based design and vehicle dynamics simulation tools such as CarSim, CarMaker, or equivalent, Working knowledge of embedded software development in C/C++, MATLAB/Simulink, and code generation for production-oriented development, Familiarity with vehicle communication and measurement tools such as Vehicle SPY, INCA, and CANalyzer, M. S. or Ph. D. in Controls, Robotics, Aerospace, Mechanical Engineering, Electrical Engineering, Computer Engineering, Applied Mathematics, or a related field with relevant experience, analytical and problem-solving skills, Demonstrated ability to communicate clearly through technical reports and presentations and to collaborate effectively across teams, Valid driver’s license for occasional test support