PhD Position in Musculoskeletal Robotic Systems

vor 2 Wochen


Zürich, Schweiz Department of Mechanical and Process Engineering Vollzeit

Job description

The doctoral research will be multidisciplinary, involving the analysis of current musculoskeletal technologies and the design of innovative robotic hands or quadrupeds. 
You will focus on: 

Analyzing existing muscle technologies and robotic systems such as hands and legged systems, studying natural mechanisms of manipulation and locomotion Designing and developing a robotic hand or leg using muscle-like actuators, tendons, ligaments, and skeletons --- aiming for superior performance compared to traditional motor-driven hands or legs Integrating sensing and proprioceptive technologies to enhance the robot's functionality and control Employing advanced control strategies and machine learning approaches, including imitation learning, for effective manipulation 

You will closely collaborate with other PhD students working on muscle technology, manipulation, control, and machine learning. 

The output of the PhD thesis will significantly contribute to the advancement of musculoskeletal robotics, particularly in the field of manipulation or locomotion, setting a new benchmark for robotics. 

Your profile

We are seeking a highly motivated, exceptional candidate with a passion for pioneering research in robotic system integration and musculoskeletal robotics. The ideal candidate should: 

Hold a master's degree in robotics or a related field such as mechanical engineering or electrical engineering Have an excellent track record and meet the general admission requirements for a doctorate at ETH Zurich Possess prior research experience in robotics, actuator development, or related areas Be experienced or have a keen interest in machine learning, control theory, and sensing technologies Demonstrate the ability to work collaboratively in an interdisciplinary team

Experience in design and fabrication of robotic systems, especially those incorporating new actuator technologies, is highly desirable. Familiarity with machine learning techniques, such as imitation learning, and a background in biological or biomimetic systems would be advantageous. 



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