Co-design of hardware and control for dexterous mobile manipulation

We are looking for a talented roboticist to join our team and take part in an exciting project on co-design of robot hardware and control, with a focus on dexterous mobile manipulation.

The project: We are creating a global optimization framework for the co-evolution of hardware (sensors, morphology, actuation, and materials) alongside machine learning methods that autonomously master contact-rich manipulation tasks. By integrating scalable adaptive simulation with active learning, the project aims to enable efficient hardware-in-the-loop optimization. The ultimate goal is to substantially reduce the cost and time for customizing robots by evolving beyond the limitations of purely anthropomorphic designs.

The ideal candidate: Someone with practical 'full-stack' robotics experience from industry or long-term involvement in academic research. We have experts in optimization, reinforcement learning, and soft robotics working with our team. We would like to bring onboard a researcher whose strengths are in robot control or mechanical engineering, who is enthusiastic about teamwork to unify individual research efforts into a strong well-performing framework.

Who can apply: This is an excellent Postdoc opportunity for recent PhD graduates, but we also welcome Master's or Bachelor's graduates with a strong robotics background.

Timeline and Duration: 1-1.5 years would be ideal, but there is also some flexibility for a shorter duration. We are looking for someone who can join very soon (in the next 2-3 months).

For further information please contact Rika Antonova.