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Mark Fuge

Professor

ETH Zurich

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Switzerland

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Research Interests

Artificial Intelligence

10%

Mathematics

10%

Self-supervised Learning

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Mechanical Engineering

10%

Optimisation

10%

Transfer Learning

10%

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Positions1

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Mark Fuge

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ETH Zurich

PhD Position in Machine Learning and Engineering Design at ETH Zurich

The Laboratory for Intelligence in Design Engineering and Learning (IDEAL) at ETH Zurich, under the guidance of Professor Mark Fuge, is seeking applications for one to two PhD positions in Machine Learning for Engineering Design. The lab is part of the Department of Mechanical and Process Engineering and focuses on the application of Artificial Intelligence and Machine Learning to engineering design challenges in domains such as healthcare, power generation, aerospace, and robotics. Research topics of interest include, but are not limited to: generative models, transfer learning, formal systems and program analysis, self-supervised learning, intersections of mathematical topology and machine learning, agentic/multi-agent coordination for engineering design, industrial robotics and multi-robot coordination, and the development of engineering benchmarks or evaluation frameworks. The lab values scientific excellence and fit over specific research topics, and encourages applicants from diverse educational backgrounds including engineering, mathematics, computer science, and physics. As a doctoral researcher, you will engage in individual and collaborative research, contribute to publications and codebases, collaborate with industry and academic partners, and assist with teaching and lab administration. The lab offers a world-class research infrastructure, a supportive and multicultural environment, and strong professional development and mentoring opportunities. Compensation and benefits are highly competitive, with working conditions that surpass those typically found in the USA, EU, or Asia. Applicants should have a relevant degree, strong English skills, and the ability to work in diverse teams. Experience in machine learning, optimization, simulation, robotics, high-performance computing, or software engineering is valued but not strictly required. Applications should include a CV, research statement, transcripts, cover letter with interview availability, and contact information for two references. The deadline for applications is March 15, 2026, with interviews and decisions before April 1, 2026. Start dates are flexible between May and November 2026. Applications must be submitted online via the ETH Zurich portal; email or postal applications will not be considered. For further information, visit the lab website or contact Mrs. Martina Koch ([email protected]) for administrative questions (no applications).

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