Postdoc: Tactile Sensing for Dexterous Robotic Hands

ETH Zurich

Zürich

On-site

CHF 90,000 - 120,000

Full time

14 days+

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Benefits offered by this job

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Sports program (ASVZ)
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Job summary

ETH Zürich’s Soft Robotics Lab invites applications for a postdoctoral position in an industry-funded collaboration. We seek a hands-on researcher to lead the development of compact, durable tactile sensors for dexterous robotic hands, enabling reliable manipulation in unstructured real-world settings.

You will design, fabricate, and characterize multi-axis sensor modules, benchmark modalities, and integrate them into a tendon-driven hand platform, while co-supervising students and publishing

Qualifications

  • PhD in robotics or closely related field.
  • Strong publication record in hardware research and real-world validation.
  • Experience with tactile sensing, force sensing or related modalities.

Responsibilities

  • Lead the design, fabrication, and characterization of compact tactile sensing modules with multi-axis force information.
  • Survey and benchmark different tactile sensing modalities.
  • Integrate sensors into a tendon-driven robotic hand platform and optimize readout electronics.
  • Design bench- and task-level benchmarks and demonstrate closed-loop tactile behaviors.
  • Co-supervise PhD and Master’s students and disseminate results through publications.

Skills

Hands-on hardware research
PhD in robotics / related field
English proficiency
Mentoring experience
Electronics design
Fabrication
Signal processing

Education

PhD in robotics, mechanical engineering, mechatronics, electrical engineering or related field

Tools

Electronics design
Fabrication

Job description

ETH Zürich’s Soft Robotics Lab invites applications for a postdoctoral position in an industry-funded collaboration. We seek a hands-on researcher to lead the development of compact, durable tactile sensors for dexterous robotic hands, enabling reliable manipulation in unstructured real-world settings.

You will design, fabricate, and characterize multi-axis sensor modules, benchmark modalities, and integrate them into a tendon-driven hand platform, while co-supervising students and publishing

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