Doctoral Positions in Robot Learning and Soft Robotics
Join ETH Zurich to build, model, and control novel soft, musculoskeletal, and biohybrid robots.
Best for:Best for engineering and computer science graduates with a passion for hands-on robotics, soft materials, or robot learning.
Overview
The Soft Robotics Lab at ETH Zurich invites applications for multiple doctoral positions focusing on soft and musculoskeletal robotics, biohybrid living systems, dexterous manipulation, and robot learning. Successful candidates will collaborate in an interdisciplinary environment to design novel robotic hardware, develop advanced control and machine learning policies, and validate their systems through real-world experiments. The position leads to a PhD degree within the D-MAVT doctoral programme, featuring comprehensive mentorship, state-of-the-art laboratory facilities, and a fully funded employment contract.
What you'll do
- Translate conceptual research ideas into working hardware and software systems through rapid prototyping and systematic experimentation
- Design and fabricate physical robot mechanisms, compliant structures, custom actuators, and integrated electronics
- Develop and deploy learning-based control policies, imitation learning frameworks, and reinforcement learning algorithms on physical robots
- Investigate bio-inspired musculoskeletal designs to achieve adaptive, robust behavior and efficient physical interaction with the environment
- Culture and bioprint biological tissue for use as controllable actuators if focusing on biohybrid systems
- Publish peer-reviewed research findings at premier international robotics and AI conferences and journals
- Release open-source hardware designs and software code to contribute to the academic community
- Supervise semester and Master's student projects and complete required doctoral coursework within the D-MAVT programme
Requirements
Degree required: Master's degree in mechanical engineering, robotics, mechatronics, electrical engineering, computer science, materials science, bioengineering, or a closely related field
Degree offered: PhD
Language: English; strong written and spoken proficiency
Instruction: English
Publications: No prior publications are strictly required, though a publication or preprint is considered a plus.
- Depth in hands-on system building and mechatronics including CAD, manufacturing, electronics, and hardware integration
- Proficiency in machine learning, control algorithms, or reinforcement learning applied to real robotic systems
- Demonstrated ability to carry projects from initial concept to functional hardware results rather than simulations alone
- Strong motivation and curiosity for interdisciplinary experimental research across robotics, materials, and biology
- Collaborative mindset with strong interpersonal skills to support team members and work across disciplines
- Applicants must hold or be close to completing a recognized Master's degree
Funding & terms
How to apply
Submit your application through the official recruitment portal by providing all required academic documents, a detailed CV, and a clear statement of interest indicating your preferred research direction.
- CV
- Cover letter
- Research statement
- BSc + MSc transcripts
- Reference letters
Contact
Create a free profile to unlock your personal match score, the full requirements, funding details and how to apply — in English or Persian.