Senior Robotics Researcher - Learning-Based Motion Planning (Contractor)

About the role

The Autonomous Robotics Research Centre (ARRC) at Technology Innovation Institute (TII) in Abu Dhabi is seeking a Senior Robotics Researcher with strong expertise in motion planning, control and mathematical optimization, and research experience applying machine learning to robotics. Working with the motion planning team lead and engineers, you will develop planning solutions for autonomous vehicles, advance model-based and learning-based methods and build reusable technology. Initial research focuses on planning under uncertain environments and obstacle estimates; sensor-to-trajectory planning is a longer-term direction.

Key responsibilities

• Develop, adapt and deliver motion-planning solutions meeting 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 limitations in prediction, modeling or planning, and justify them against model-based alternatives.
• Design experiments using simulation and robot data, including data preparation and model training.
• 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, working with engineers on integration, testing and deployment.
• Mentor engineers and students, collaborate across application teams, and lead publications and technical reports in robotics, motion planning and control.

Required profile

• Ph.D. in Robotics, Control, Applied Mathematics, Computer Science, Engineering or a related field, or equivalent demonstrated research experience.
• 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 in robotics or control (e.g., ICRA, IROS, IEEE RA-L, IEEE Transactions on Robotics, IEEE TAC, Automatica, CDC, NeurIPS, ICML, ICLR).
• Desirable: experience developing planning or control algorithms on real autonomous vehicles (ground, marine or UAV), imitation learning, reinforcement learning, temporal models, sensor-to-trajectory planning, ROS 2, real-time implementation, simulation-to-reality transfer or deployment on robotic hardware.

Required skills

• Strong programming in C++ and/or Python, implementing and testing planning algorithms and using an ML framework such as PyTorch.
• Strong foundation in motion planning, control theory, robot kinematics and dynamics, mathematical optimization, probability and numerical methods.