Key Responsibilities

• Investigate hybrid approaches combining learning-based and control-theoretic methods for motion planning and control.
• Design and implement algorithms using DRL, imitation learning, and/or trajectory optimization techniques.
• Conduct experiments using simulation environments or real-world robotic platforms.
• Analyze and benchmark performance, safety, and generalization of proposed methods.
• Collaborate with research mentors and team members to prepare a publishable research outcome.
• Present findings to internal teams and publish a paper to a top tier journal/confernce.

Minimum Qualifications

• Currently enrolled in a PhD program in Robotics, Computer Science, Electrical Engineering, or a related field.
• First-author publications in leading conferences or journals related to deep learning, motion planning, or robotics.
• Solid understanding of reinforcement learning, optimal control, and motion planning.
• Proficiency in Python and deep learning frameworks such as PyTorch or TensorFlow.
• Experience working with simulation tools or real-world robotics environments.

Bonus Qualifications

• Research experience in autonomous driving, micromobility, or related domains.
• Familiarity with simulation environments such as CARLA, Metadrive, NVIDIA Isaac, AirSim, or similar platforms.
• Experience working with datasets relevant to motion planning and autonomous driving, including the Waymo Open Dataset, nuScenes, Argoverse, or KITTI.
• Experience combining data-driven and model-based control strategies.
• Proficiency in C++ and/or ROS for real-time system development.
• Knowledge of uncertainty modeling, risk-aware planning, or interpretability in RL/control.