We are looking for robotics researchers to work on learning-based robot policies for manipulation tasks involving single-arm and dual-arm robotic systems. The role involves contributing across the full development lifecycle of robotic behaviours, from data collection through to real-world deployment.
Work may include, but is not limited to:
- Developing methods for acquiring and processing robot training data, including teleoperation and simulation-based approaches
- Designing and training models capable of achieving high success rates in real-world robotic manipulation tasks
- Improving and optimising the performance of robotic systems already deployed in operational environments
We are particularly interested in candidates with experience in robot learning or manipulation research, ideally at PhD level. Strong prior work in robotics research is highly valued, though relevant experience in adjacent areas of machine learning, control systems, or embodied AI will also be considered.