ETH Zürich

Research Assistant in Generative AI for Morphology Optimization of Assistive Devices

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ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

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Research Assistant in Generative AI for Morphology Optimization of Assistive Devices

Are you a highly motivated and enthusiastic researcher looking to make a difference in the field of AI applied in Healthcare? Join us at the Spinal Cord Injury Artificial Intelligence - SCAI Lab at ETH Zurich.

Our team of clinical and research scientists is dedicated to improving healthcare systems using physiological and clinical information analysis for a closed-loop decision support systems in rehabilitation in many health conditions. 

This position is open for a research engineer in the field of generative design for exoskeletons and orthosis. Focused on developing a novel method for autonomous design of 3D shapes fitting to the user body and optimizing the design to the desired assistance.

Dr Diego Paez-Granados in collaboration with Prof. Robert Riener (SMS Lab) will supervise the successful engineer. 

The position is fully funded for 2 years. A potential extension to a doctoral position can be considered upon performance.
Ideal starting date: July 2024 (or shortly thereafter).

Project background

The goal of this project is to leverage advanced machine learning to develop an automated design process of mechanical walking aids, analyse gait patterns and make biomechanical simulations embedded in the generative mechanism design process. As a team member, you will experience a range of exciting challenges, including the development of innovative technologies for patient usage, and developing standardization methods for customizing aids.

You will be based at ETH Zurich and collaborate with the team at the Swiss Paraplegic Center (SPZ) in Nottwil and an industrial in our shared project.

Job description

Wiithin thjs project you will leverage advanced 3D shape construction through generative design for the structural improvement of walking aids and biomechanical fitting to the user morphology, developing a software solution that integexpert knowledge included in the design and fabrication. 

If you are a highly motivated and creative individual with a passion for innovation, we want to hear from you.

Join us and help shape the future of 3D design and additive manufacturing for mechanical and mechatronic aids.

Profile

  • You have experience in mesh, and CAD with a Master's degree from a university in Robotics, Mechanical Engineering, Electrical Engineering, or related fields,
  • Highly motivated, self-driven, and shows excellent performance in your studies.
  • CAD, 3D modelling and experience in shape analysis such as surface and topology consistency (preferred)
  • Confirmed records on one of the methodological areas: deep learning, topological design, generative AI. (preferred)
  • Proven track record in deploying machine learning models into production (C/C++/C# or Python)
  • You have first-rate oral and written English skills.
  • Highly organized with excellent communication and interpersonal skills.

We offer

To become part of an ambitious, research team offering an outstanding research environment, full access to extensive facilities and great team spirit in close collaboration with international academia and industrial partners.

This is a unique opportunity to be at the forefront of cutting-edge research in AI for human modelling and fitting, working with state-of-the-art technology and collaborating with a highly motivated research and industrial team.

We offer a full-time research assistant/engineer position (100% or as agreed ) with a competitive salary in accordance with ETH standards. Work location ETH Zurich / Nottwil with flexibility for remote work possible.

Working, teaching and research at ETH Zurich

We value diversity

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.

Curious? So are we.

We look forward to receiving your online application with the following documents as a single file:

  1. CV (2 pages max),
  2. Statement of interests and self-assessment of your profile match (1 page Max) with the subject line: "AI in healthcare is transparent or null"
  3. One code example with documentation,
  4. Transcripts of all degrees obtained (in English)
  5. A letter of recommendation
  6. 2 referees' contact information

Applications will be processed regularly until the position is filled.

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 HEST can be found on our website.

Questions regarding the position should be submitted to Mrs. Ai Sullivan, Email ai.sullivan@hest.ethz.ch (no applications).

About ETH Zürich

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.

Job details

Title
Research Assistant in Generative AI for Morphology Optimization of Assistive Devices
Employer
Location
Rämistrasse 101 Zurich, Switzerland
Published
2024-06-20
Application deadline
Unspecified
Save job

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About the employer

ETH Zürich is well known for its excellent education, ground-breaking fundamental research and for implementing its results directly into practice.

Visit the employer page

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