Postdoc Fellow: AI for Engineering Design

Immigration Policy Lab; Zürich; On-site; CHF 90,000 - 110,000; Full time

Job summary

The Laboratory for Intelligence in Design Engineering and Learning (IDEAL) at ETH Zurich invites applications for a Post-doctoral position in Machine Learning for Engineering Design under Prof. Mark Fuge.

The role focuses on AI-guided design, inverse design, and optimization across engineering domains, integrating physical experiments with AI models and formal verification. The successful candidate will lead research, work with PhD students and staff, publish high-impact papers, and contribute

Qualifications

• Ph.D. in a relevant field with a strong publication record.
• Strong English and ability to collaborate in multinational teams.
• Experience with ML or HPC techniques and Python/Julia libraries.

Responsibilities

• Develop or analyze novel algorithms for engineering design applications.
• Collaborate with industry, academia, and national labs to translate real-world needs into questions.
• Deploy simulation or algorithm pipelines on HPC systems.
• Lead development efforts and coordinate multiple contributors.
• Set up digital or physical infrastructure to support experiments and lab processes.
• Disseminate results through papers, conferences, and outreach.
• Contribute to research proposals and supervise students.
• Assist in teaching activities related to the lab's focus.

Skills

Machine Learning

Robotics

Computational Simulations

HPC

Software Engineering

Education

PhD in Mechanical Engineering

PhD in Computer Science

PhD in Applied Mathematics

PhD in Robotics

Tools

PyTorch

JAX

Docker

Singularity

Job description

The Laboratory for Intelligence in Design Engineering and Learning (IDEAL) at ETH Zurich invites applications for a Post-doctoral position in Machine Learning for Engineering Design under Prof. Mark Fuge.

The role focuses on AI-guided design, inverse design, and optimization across engineering domains, integrating physical experiments with AI models and formal verification. The successful candidate will lead research, work with PhD students and staff, publish high-impact papers, and contribute