Student Research Assistant - Scientific Machine Learning Engineering Systems

We are looking for motivated student research assistants to join our project "Adaptive Physics-Informed Neural Operators with Reduced-Order Modeling for Complex Dynamical Systems." Funded through an ETH Zurich Career Seed Award, this project aims to develop scientific machine learning frameworks that integrate physics-based modeling with neural network architectures. The goal is to build efficient and robust computational tools for analyzing complex engineering systems.

Applications include structural dynamics and other dynamical systems relevant to real-world engineering challenges. The work is conducted at the interface of mechanics, artificial intelligence, and computational science. The developed methods will be validated on benchmark problems and real-world data and disseminated through open-source code and scientific publications.

Start: As soon as possible (applications reviewed on a rolling basis)

Duration: 6 months (extendable)

• Apply computational and machine learning approaches to model engineering systems
• Perform simulations and validate models on benchmark and experimental data
• Improve and extend methodologies based on numerical and experimental findings
• Contribute to research software development and documentation

• Master’s student at ETH Zurich (Civil Engineering, Mechanical Engineering, Computer Science, Applied Mathematics, or related disciplines)
• Good programming skills (Python, MATLAB)
• Interest in machine learning (ML) and structural mechanics
• Motivation to work on interdisciplinary research problems
• Independent and structured working style

• Opportunity to contribute to cutting-edge research at ETH Zurich
• A collaborative and supportive research environment
• Flexible working hours compatible with your academic schedule
• Standard ETH student research assistant salary

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