The future of robotic manipulation lies in the development of systems that closely mimic the complex structures and capabilities of the human hand. Traditional motor-driven robotic hands, while effective for a range of tasks, lack the versatility, sensitivity, and adaptability of their biological counterparts. This project focuses on bridging this gap by leveraging musculoskeletal robot technologies. You will embark on a comprehensive study of existing muscle-like technology and robotic hands, drawing inspiration from nature to design and develop a robotic hand that outperforms classic designs in terms of dexterity, efficiency, and functionality. This involves integrating advanced muscle and tendon systems, incorporating cutting-edge sensing and proprioceptive technologies, and controlling the hand through sophisticated control algorithms and machine-learning techniques. 

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

• 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 

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. 

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 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. 

ETH offers an exciting opportunity, and admission is highly competitive. The fellows will be employed according to the regulations of ETH Zurich. Upon completing the doctorate, the fellows are awarded the title "Doctor of Sciences (Dr. sc. ETH Zurich)".

ETH Zurich is a family-friendly employer with excellent working conditions. You can expect an exciting working environment, cultural diversity, and attractive offers and benefits.

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish.

We look forward to receiving your online application with the following documents in a single merged PDF document, titled with your last name and initials as well as the application date (for example, 20240301_DoeJane_application) in the following order:

1. Cover letter with a description of your research achievements and research interests
2. Detailed CV
3. Transcripts of all degrees (English)
4. Names and contact information of at least three references
5. Representative published research work (e.g., papers and thesis)

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

Further information about our group can be found on our website. Questions regarding the position should be directed to Ronan Hinchet under the email ronan.hinchet@srl.ethz.ch (no applications).

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.