PhD in Machine Learning for Engineering Design & Innovation

ETH Zürich; Zürich; On-site; CHF 55,000 - 75,000; Full time

Benefits offered by this job

Career development

Mentoring and growth

Diverse team

Job summary

ETH Zürich's IDEAL Lab invites applications for one to two doctoral positions in Machine Learning for Engineering Design under Prof. Mark Fuge. This general search prioritizes scientific excellence and fit to the lab with focus areas spanning ML, optimization, robotics and design challenges.

You will conduct independent or collaborative research, publish findings, and collaborate with industry, academia, and national labs while pursuing relevant coursework and mentoring students.

Qualifications

• Open to diverse educational backgrounds (engineering, mathematics, CS, physics or others).
• Interest or expertise in ML, optimization, simulation or robotics.
• Strong publication record is a plus but not required, especially for non-traditional paths.
• Experience with HPC environments or software engineering is a plus but not required.
• Strong English language skills and ability to collaborate in multinational teams.

Responsibilities

• Conduct individual or collaborative research, write publications and contribute to code bases.
• Collaborate with industry, academia, and national labs to translate real-world needs into questions.
• Learn new graduate courses or via self-learning to extend skills.
• Assist with the lab’s teaching mission and supervise Master/Bachelor thesis students as needed.
• Support shared administrative tasks and contribute as a good lab citizen.

Skills

Machine Learning

Optimization

Simulation

Robotics

Job description

ETH Zürich's IDEAL Lab invites applications for one to two doctoral positions in Machine Learning for Engineering Design under Prof. Mark Fuge. This general search prioritizes scientific excellence and fit to the lab with focus areas spanning ML, optimization, robotics and design challenges.

You will conduct independent or collaborative research, publish findings, and collaborate with industry, academia, and national labs while pursuing relevant coursework and mentoring students.