Doctoral Position in Soft and Musculoskeletal Robotics, Biohybrid Systems, and Robot Learning

Job description Depending on your direction, your work will emphasize different parts of the following. All of it happens in a lab where hardware, biology, and learning sit in the same room. 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 Each design cycle feeds the next, so rapid prototyping, measurement, and iteration sit at the heart of every project Drawing inspiration from biological musculoskeletal systems, you will engineer how bones, joints, tendons, and muscles can be recreated with compliant materials, artificial actuators, or living tissue, and how their interplay produces strength, dexterity, and robustness You will build robots that derive much of their capability from their embodiment, achieving rich, adaptive behavior with less reliance on complex centralized control If your focus is learning, you will develop control and perception methods that work on real, compliant, contact-rich hardware, not only in simulation, and you will help define the benchmarks that make such claims measurable If your focus is biohybrid systems, you will work in our biological laboratories at ETH, culturing and bioprinting tissue and turning it into a controllable actuator You will publish at the top venues in the field, release open-source hardware and code where it helps the community, and present your work internationally Close, day-to-day collaboration across our hardware, muscles, and machine learning teams is expected and is what makes this work possible

Job description

Depending on your direction, your work will emphasize different parts of the following. All of it happens in a lab where hardware, biology, and learning sit in the same room. 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 Each design cycle feeds the next, so rapid prototyping, measurement, and iteration sit at the heart of every project Drawing inspiration from biological musculoskeletal systems, you will engineer how bones, joints, tendons, and muscles can be recreated with compliant materials, artificial actuators, or living tissue, and how their interplay produces strength, dexterity, and robustness You will build robots that derive much of their capability from their embodiment, achieving rich, adaptive behavior with less reliance on complex centralized control If your focus is learning, you will develop control and perception methods that work on real, compliant, contact-rich hardware, not only in simulation, and you will help define the benchmarks that make such claims measurable If your focus is biohybrid systems, you will work in our biological laboratories at ETH, culturing and bioprinting tissue and turning it into a controllable actuator You will publish at the top venues in the field, release open-source hardware and code where it helps the community, and present your work internationally Close, day-to-day collaboration across our hardware, muscles, and machine learning teams is expected and is what makes this work possible

Depending on your direction, your work will emphasize different parts of the following. All of it happens in a lab where hardware, biology, and learning sit in the same room.

• 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
• Each design cycle feeds the next, so rapid prototyping, measurement, and iteration sit at the heart of every project
• Drawing inspiration from biological musculoskeletal systems, you will engineer how bones, joints, tendons, and muscles can be recreated with compliant materials, artificial actuators, or living tissue, and how their interplay produces strength, dexterity, and robustness
• You will build robots that derive much of their capability from their embodiment, achieving rich, adaptive behavior with less reliance on complex centralized control
• If your focus is learning, you will develop control and perception methods that work on real, compliant, contact-rich hardware, not only in simulation, and you will help define the benchmarks that make such claims measurable
• If your focus is biohybrid systems, you will work in our biological laboratories at ETH, culturing and bioprinting tissue and turning it into a controllable actuator
• You will publish at the top venues in the field, release open-source hardware and code where it helps the community, and present your work internationally
• Close, day-to-day collaboration across our hardware, muscles, and machine learning teams is expected and is what makes this work possible