PhD in ML for Engineering Design – Zurich

ETH Zurich; Zürich; On-site; CHF 50,000 - 60,000; Full time

Benefits offered by this job

World-class research infrastructure

Diverse, motivated multicultural team

Excellent working conditions

Professional development and mentoring

Strong track record of placing staff

Collaboration with global scholars

Job summary

ETH Zurich's IDEAL Lab invites applications for one to two doctoral positions in Machine Learning for Engineering Design. You will work under Prof.

Mark Fuge on ML methods for design problems across healthcare, power, aerospace and robotics, with opportunities to publish and contribute code. The role includes collaboration with industry and national labs, teaching support for Masters and Bachelors theses, and a collegial, multicultural team.

Qualifications

• Open to diverse educational backgrounds including engineering, mathematics, computer science, physics or other degree backgrounds.
• Candidates typically have interests in or expertise in machine learning, optimization, simulation or robotics.
• Strong publication record is a plus but not required; practical experience or non-traditional paths accepted.

Responsibilities

• Individual or collaborative research including writing publications and contributing toward code bases.
• Collaborating with industry, academia, and national labs to translate real-world needs into scientific questions.
• Learning new skills via courses, graduate offerings, or self-learning.
• Assisting with the teaching mission of the lab, including Masters/Bachelors thesis supervision and course support as needed.
• Participating in shared lab administrative tasks and contributing to a collegial lab culture.

Job description

ETH Zurich's IDEAL Lab invites applications for one to two doctoral positions in Machine Learning for Engineering Design. You will work under Prof.

Mark Fuge on ML methods for design problems across healthcare, power, aerospace and robotics, with opportunities to publish and contribute code. The role includes collaboration with industry and national labs, teaching support for Masters and Bachelors theses, and a collegial, multicultural team.