
Post-Doctoral Associate in Robot Learning and Control Laboratory in the Division of Engineering
- Abu Dhabi
- Permanent
- Full-time
- Robot Learning & Autonomy - Developing algorithms that allow robots to learn (via exploration or imitation) from interaction, adapt to new tasks, and generalize across environments.
- Dexterous Manipulation - Advancing robotic grasping, in-hand manipulation, and adaptable object handling in complex real-world scenarios, including those involving deformable objects and uncertain conditions..
- Semantic Navigation & Mapping - Designing solutions for robot navigation, for Mobile Manipulators, that integrate environmental semantics and spatial reasoning.
- AI for Physical Systems - Leveraging machine learning and AI to improve performance, safety, and adaptability of robots, autonomous vehicles, and other intelligent machines.
- Safe and Certified Control for manipulation: Designing control algorithms that ensure passivity, Lyapunov stability, and safety for human-robot collaboration.
- Strong focus on robot manipulation learning & control, and differential geometry is a plus.
- Knowledge of safe control methods including Lyapunov stability, barrier functions, and certified learning frameworks.
- Hands-on experience with robotic manipulators (preferebly Franka Emika R3 robots) and real-world experimentation.
- Demonstrate a high degree of self-motivation, research-oriented thinking, and a drive to operationalize robotic systems for real-world tasks.
- Strong publication record in top-tier venues: ICRA, IROS, RA-L, T-RO, IJRR, CoRL, NeurIPS, RSS, CDC, TAC.
- Proficiency in Python, C++, ROS, and machine learning frameworks such as PyTorch or TensorFlow.
- Excellent communication and collaboration skills, with the ability to work effectively in an interdisciplinary research environment.
Times Higher Education