Research Scientist in Scientific AI and Geotechnical Engineering

**Research Scientist in Scientific AI and Geotechnical Engineering** ==================================================================== #### **100%, Zurich, fixed-term** The Chair of Geotechnical Engineering at ETH Zurich, part of the Institute for Geotechnical Engineering (IGT), focuses its teaching and research on foundation engineering, soil mechanics and natural hazards. Our objective is to combine theoretical knowledge with experiments and numerical simulations to develop new methods for understanding and predicting the behaviour of soils, foundations and infrastructure. We are opening a position in our research group at ETH Zurich, focusing on developing and integrating modern Artificial Intelligence (AI) methods into geotechnical engineering research, while maintaining a strong connection to the underlying physics and mechanics. We do not aim to develop a new AI model or replace physics-based engineering with purely data-driven AI. Instead, we want to use AI to transform the way research itself is performed—from numerical model generation and experimental data acquisition to analysis, visualization and communication. For this purpose, we are looking for a **Research Scientist in Scientific AI and Geotechnical Engineering,** who will work across the research group, collaborating with PhD students, postdoctoral researchers and senior scientists. The role combines scientific research, computational engineering, research automation, data science and the development of reusable AI-enabled tools. A central objective is to identify new opportunities where AI can enable research that would otherwise be difficult or impossible, and to develop capabilities that can be used across the entire group. **Job description** ------------------- The job will interconnect several different areas of our activity: **Scientific AI and physics-informed modelling** * Develop and apply AI and machine-learning approaches to challenging problems in geotechnical engineering and computational mechanics, with particular emphasis on approaches that combine data with physical knowledge, rather than relying exclusively on black-box, data-driven models. * Explore topics including physics-informed and physics-guided machine learning, hybrid physics–ML constitutive modelling, surrogate and reduced-order models, inverse problems and parameter identification, uncertainty quantification, and AI-assisted scientific discovery. * Apply these methods to problems involving soil behaviour, soil–structure interaction, liquefaction, natural hazards, foundations and infrastructure resilience. **AI-enabled numerical modelling** * Develop tools for automating and accelerating numerical modelling workflows, including finite element (FE) and potentially discrete element (DEM) and material point method (MPM) simulations. * Automate tasks, such as model generation, geometry and mesh creation, assignment of materials and boundary conditions, parameter studies, high-performance comp