ETH Zurich
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Doctoral Position in Machine Learning for Engineering Design ETH Zurich in Switzerland
Degree Level
PhD
Field of study
Computer Science
Funding
Available
Deadline
Jan 15, 2027
Country
Switzerland
University
ETH Zurich
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About this position
The Laboratory for Intelligence in Design Engineering and Learning (IDEAL) at ETH Zurich invites applications for one to two doctoral positions in Machine Learning for Engineering Design, under the guidance of Professor Mark Fuge, Chair of Artificial Intelligence in Engineering Design.
The laboratory studies artificial intelligence and machine learning in engineering design, with applications spanning healthcare, power generation, aerospace, and robotics. This is a general search rather than a project-specific opening, so doctoral researchers can develop research directions that align with their interests and the laboratory’s scientific strengths.
Current research areas include self-supervised and transfer learning; industrial robotics and multi-robot coordination, especially design for robotic assembly and manufacturing; agentic and multi-agent coordination for engineering design; formal systems and program analysis; the intersection of mathematical topology and machine learning; and the development of engineering benchmarks and evaluation frameworks. The lab also welcomes emerging research at the intersection of machine learning, artificial intelligence, and engineering design.
Doctoral researchers may conduct individual or collaborative research, write publications, contribute to shared codebases, and work with partners from industry, academia, and national laboratories. They may also develop specialized graduate skills, assist with teaching and student theses, and participate in shared laboratory activities.
IDEAL provides a multicultural and intellectually rich research environment with world-class infrastructure, collaborative colleagues, professional development support, and mentoring. The lab is part of ETH Zurich’s Department of Mechanical and Process Engineering and offers opportunities to engage with researchers across engineering, computer science, mathematics, and related fields.
Funding details
Available
What's required
Applicants may come from engineering, mathematics, computer science, physics, or another relevant academic background. Relevant interests or expertise include machine learning, optimization, simulation, or robotics. A strong publication record is advantageous but not required, particularly for applicants with practical experience or non-traditional career paths. Experience with high-performance computing environments or software engineering best practices is advantageous but not required. Strong English language skills and the ability to work collaboratively in diverse, multinational teams are expected. The application must include a CV or resume with relevant experience and publications if applicable, a 1–2-page research statement or statement of purpose, undergraduate and graduate transcripts, a cover letter with interview availability and desired start date, and contact information for two references.
How to apply
Submit the required documents as PDFs through the online application portal by January 15, 2027. Include your CV, research statement, transcripts, cover letter, and two references; applications by email or post are not accepted.
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Prepare your application
Review drafts and confirm requirements with the university.
Make your next move.
Prepare your application
Review drafts and confirm requirements with the university.