- Analyzing existing muscle technologies and robotic hand systems, studying natural mechanisms of manipulation
- Designing and developing a robotic hand using muscle-like actuators and tendons, aiming for superior performance compared to traditional motor-driven hands
- Integrating sensing and proprioceptive technologies to enhance the hand's functionality and control
- Employing advanced control strategies and machine learning approaches, including imitation learning, for effective manipulation
- 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
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ETH Zürich Zurich, Schweiz TEMPORARYPhD Position in Robotic System Integration for Musculoskeletal Robotic Hands · 100%, Zurich, fixed-term · print Drucken · Project background · The future of robotic manipulation lies in the development of systems that closely mimic the complex structures and capabilities of the ...
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PhD Position in Robotic System Integration for Musculoskeletal Robotic Hands - Zürich, Schweiz - Department of Mechanical and Process Engineering
Beschreibung
Job description
The doctoral research will be multidisciplinary, involving the analysis of current musculoskeletal technologies and the design of innovative robotic hands.
You will focus on:
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, setting a new benchmark for robotic hands.
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:
Experience in design and fabrication of robotic systems, especially those incorporating novel 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.
