Senior Robotics Researcher Learning Based Motion Planning - Contractor

About the Role
We are seeking a Senior Robotics Researcher with strong expertise in motion planning, control, and mathematical optimization, complemented by research experience applying machine learning to robotics. Working with the motion planning (MPLAN) Team Lead and engineers, you will develop and deliver planning solutions for autonomous vehicles, advance model-based and learning-based methods, and build reusable technology. Initial research will address planning under uncertain environment and obstacle estimates; sensor-to-trajectory planning is a longer-term direction.

Key Responsibilities

• Develop, adapt, and deliver motion-planning solutions that meet agreed client requirements.
• Formulate and solve behavior, path, and trajectory planning problems, accounting for vehicle kinematics, dynamics, environmental constraints, and uncertainty.
• Develop and evaluate model-based planning and constrained trajectory-optimization methods, including search-based approaches, optimal control, and model predictive control.
• Investigate learning-based and hybrid methods to address identified limitations in prediction, modeling, or planning, and justify their benefits against model-based alternatives.
• Design focused experiments using simulation and robot data, including data preparation and model training where learning is appropriate.
• Evaluate planning performance, robustness, constraint satisfaction, and computational efficiency through reproducible closed-loop experiments and comparative studies.
• Transfer validated methods into documented, reusable MPLAN software; work with engineers on integration, testing, and deployment.
• Mentor engineers and students, collaborate across application teams, and contribute to technical direction.
• Lead high-quality publications and technical reports in robotics, motion planning, and control.

Required Qualifications and Skills

• Ph.D. in Robotics, Control, Applied Mathematics, Computer Science, Engineering, or a related field, or equivalent demonstrated research experience.
• Strong foundations in motion planning, control theory, robot kinematics and dynamics, mathematical optimization, probability, and numerical methods.
• Demonstrated research and implementation experience in model-based motion planning, optimal control, or constrained trajectory optimization for robotic systems.
• Research experience applying machine learning to motion planning or control, with implemented results and a clear understanding of its benefits and limitations.
• Strong publication record with substantial contributions in robotics or control, such as ICRA, IROS, IEEE RA-L, IEEE Transactions on Robotics, IEEE TAC, Automatica, CDC, NeurIPS, ICML, and ICLR, or equivalent venues.
• Strong programming skills in C++ and/or Python, with experience implementing and testing planning algorithms and using an ML framework such as PyTorch or equivalent.
• Experience evaluating algorithms in closed-loop robotic simulation, including failure analysis, model mismatch, and computational constraints.

Desirable Experience

• Experience developing, integrating, or testing planning or control algorithms on real autonomous vehicles (ground, marine, or UAV), with an understanding of vehicle dynamics.
• Imitation learning, reinforcement learning, or temporal models applied to robotics; experience with sensor-to-trajectory planning is a plus.
• ROS 2, real-time implementation, simulation-to-reality transfer, or deployment of planning algorithms on robotic hardware.

Why Join Us?
At the company, we help society to overcome its biggest hurdles through a rigorous approach to scientific discovery and inquiry, using state-of-the-art facilities and collaboration with leading international institutions. Our rigorous discovery and inquiry-based approach helps to forge new and disruptive breakthroughs in advanced materials, autonomous robotics, cryptography, digital security, directed energy, quantum computing and secure systems.