About the role
· Design and implement advanced control algorithms (e.g., PID, adaptive control, model predictive control) tailored for autonomous robotic platforms.
· Optimize motion planning and path control strategies to enhance multi-robot coordination and operational efficiency.
· Develop real-time control architectures that leverage edge computing for low-latency decision-making.
· Integrate adaptive learning techniques to continuously refine control performance, particularly for dynamic systems.
· Develop and test control algorithms in both simulation and real-world experiments, utilizing aerial simulation tools (e.g., PX4 SITL, AirSim) when applicable.
· Collaborate with interdisciplinary teams to integrate control systems with AI, sensor fusion, and hardware subsystems.
Requirements
Required Qualifications
· Master’s or PhD in Robotics, Control Systems, or a related field.
· 3+ years of experience in robotics control system development or equivalent R&D experience (PhD candidates with strong research contributions are encouraged to apply).
· Strong background in control theory, state estimation, and optimization for autonomous systems.
· Proficiency in C++, Python, and ROS; hands-on experience with real-time control systems is essential.
· Expertise in traditional model-based control methods is required; familiarity with learning-based control approaches (e.g., reinforcement learning, adaptive control) is a plus.
· Experience in aerial robotics control—including familiarity with flight dynamics and autopilot systems (e.g., PX4)—is highly desirable.
Preferred Qualifications
· Postdoctoral experience in robotics or control systems.
· Experience with real-world deployment and flight testing of autonomous systems.
· A strong research record in control theory, AI-driven robotics, or multi-agent coordination with an emphasis on aerial applications.
· Proficiency with MATLAB/Simulink for control system design and testing.
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