Doctoral Positions in Robot Learning and Soft, Musculoskeletal, and Biohybrid Robotics

100%, Zurich, fixed-term

Today's robots are mostly rigid, fragile, and a world apart from the agility and resilience of biological bodies. Our bet is that the next generation of robots will be soft, musculoskeletal, and in part alive. They will be built to make contact with the real world rather than to avoid it. We pursue this across four directions, and a strong candidate will find a home in one of them and borrow from the others.

Soft and musculoskeletal robotics. We build bodies from compliant structures, bones, joints, and tendon-like actuation. Our electrohydraulic musculoskeletal leg jumps, moves fast, and adapts to terrain at roughly 1.2% of the energy a motor-driven leg needs (Nature Communications, 2024). Our low-voltage HASEL actuators run near 1100V, are safe to touch, and work untethered and underwater (Science Advances, 2024). We recently extended these muscles to full antagonistic motion ranges (ICRA
2025) and to a sensorless, inherently compliant anthropomorphic hand driven entirely by electrohydraulic actuation (IROS
2026).

Biohybrid living systems. We grow engineered muscle and use it to actuate machines. We bioprinted multicellular muscle-tendon units that transmit force along a real musculoskeletal path (Science Advances, 2025), embedded sensors directly into muscle for closed-loop control of proprioceptive biohybrid robots (Advanced Intelligent Systems, 2025), and established functional volumetric bioprinting with xolography (Advanced Materials, 2026). Co-optimized volumetric muscle designs for large dynamic deformations are in press at Nature Communications (Balciunaite etal.,

2026). The same fabrication line reaches clinical work: with University Hospital Zurich we printed implantable reinforced cardiac tissue patches (Advanced Materials, 2025).

Dexterous manipulation and robot learning. We build hands and the policies that run them. One of our initial hand designs is now commercialized through our spin-off Mimic Robotics. ORCA is our open-source, reliable, and cost-effective anthropomorphic hand for uninterrupted dexterous task learning (IROS
2025). On that hardware we work on imitation learning and diffusion policies, cross-embodiment skill transfer through latent action diffusion (ICRA
2026), sample-efficient reinforcement learning and policy fine-tuning directly on the real robot, vision-language-action models for contact-rich tasks, and tactile representation learning on our high-resolution sensorized skin (ICRA
2024). We also build controllable dexterous world models for training and evaluation, and a benchmark of dexterity for anthropomorphic hands. Whichever side you come from, the offer is the same: the hand, the skin, the simulator, and the people who designed all three sit in one room. Build a mechanism here and someone will have a policy running on it within weeks.

Build a policy here and you can change the mechanism when the mechanism is what is wrong.

Simulation, fabrication, and embodied AI. Building these robots requires tools that did not exist. Vision-Controlled Jetting prints rigid skeletons, soft tissue, tendons, and sensors in one pass, including a full musculoskeletal hand and forearm (Nature, 2023). We close the sim-to-real gap with learned residual physics (RA-L, 2024, Best Paper Award), and we released SORS, a modular high-fidelity soft-robot simulator, at Robo Soft
2026.

Underwater and aerial systems run through all of this, from SoFi and tendon-driven swimmer digital twins to our open-source soft aerial manipulation platform (CoRL
2024).

• You will take a research idea from concept to a working system: designing the architecture, building it, integrating sensing and control, and validating it through systematic real-world experiments
• If your focus is systems design, you will design and build the robots themselves: mechanisms and compliant structures, actuators and tendon routing, embedded electronics and motor control, and the sensor integration that makes a machine measurable. You will own a system from CAD through fabrication to hardware that still runs six months later
• If your focus is learning, you will develop policies and perception that run on real, compliant, contact-rich hardware: imitation learning, real-world reinforcement learning and fine-tuning, sim-to-real transfer, and multimodal representations that use touch as well as vision. You will help define…