Research Jobs in Mountain View, CA

Key Responsibilities

• Develop, implement, and test novel algorithms for autonomous driving, focusing on uncertainty- and interaction-aware motion planning to address perception noise, state uncertainty, and multimodal motion prediction uncertainty in dynamic environments involving multiple vehicles and pedestrians, within a model predictive control (MPC) framework.
• Extend the MPC-based motion planning stack to incorporate uncertainty modeling (state, model, multimodal prediction), socially acceptable interaction-aware behaviors, real-time interactions, partial observability and occlusion.
• Evaluate the performance, safety, and robustness of the proposed algorithms in simulation environments, including uncertain dynamic scenarios involving pedestrians and other vehicles.
• Collaborate with HRI scientists and engineers to integrate findings into practical applications.
• Submit research findings to leading academic conferences or journals.

Minimum Qualifications

• Ph.D. candidate in Robotics, Computer Science, Mechanical Engineering, or similar fields.
• Strong background and experience in at least one of the following: motion planning and decision-making under uncertainty, interaction-aware motion planning, occlusion-aware planning, active learning, or reinforcement learning.
• Experience with model predictive control (MPC).
• Programming and software design skills in C++ and Python.
• Self-motivated and able to work independently.

Bonus Qualifications