Research Scientist in Scientific AI and Geotechnical Engineering

Research Scientist in Scientific AI and Geotechnical Engineering

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 computing (HPC) execution, model verification, extraction of results and standardised post-processing.
• Create robust and reusable tools that enable researchers throughout the group to build and analyse numerical models more efficiently while retaining appropriate engineering control and verification.

Automation of experimental research

• Develop AI-enabled methods for the acquisition, processing and interpretation of experimental data from laboratory and centrifuge testing.
• Automate sensor-data processing, synchronization of different measurement systems, computer-vision analysis of experiments, extraction of deformation and failure mechanisms from images and videos, identification of experimental parameters, and integration of experimental and numerical results.

Geotechnical data intelligence

• Develop methods to exploit the growing volume of geotechnical data available within the group and through external repositories.
• Organize and extract information from historical laboratory and centrifuge experiments and numerical analyses, and exploit large external datasets and research repositories such as DesignSafe and databases containing geotechnical and geological properties.
• Contribute to the longer-term development of structured, physics-aware geotechnical datasets to support parameter inference, model calibration, uncertainty quantification, identification of model deficiencies and the development of new physics-informed AI approaches.

Scientific visualisation and communication

• Develop automated approaches for transforming research results into high-quality visualisations, animations and videos.
• Develop tools that support scientific publications and presentations while facilitating effective communication of research through websites, teaching materials, professional audiences and social-media platforms.

Group-wide AI capability

• Help establish AI as a broadly accessible research capability within the group.
• Identify opportunities for AI across ongoing research activities, develop common tools and workflows, provide internal training and documentation, and work directly with researchers to enable them to use modern AI and computational methods effectively and independently.

Profile

We are looking for an intellectually curious scientist / engineer who combines strong computational skills with an interest in physical systems and engineering research. A background in geotechnical engineering is not mandatory but can represent an advantage.

• PhD in computational mechanics, computational engineering, scientific machine learning, applied mathematics, computer science, civil or mechanical engineering, or a closely related discipline.
• Excellent programming skills.
• Strong background in numerical methods, such as FEM, FD, DEM or MPM.
• Demonstrated experience with machine learning, modern AI methods and AI-assisted software development.
• Experience with scientific data engineering, databases and the processing of large or heterogeneous datasets.
• Experience with HPC, parallel computing and/or GPU computing.
• Experience with automated simulation workflows and APIs.
• Experience with scientific visualisation and the automated generation of figures, animations or videos.
• Experience with computer vision and image/video analysis is particularly valuable.

Candidates from other fields than geotechnical engineering with strong expertise in computational mechanics and Scientific AI are strongly encouraged to apply. The successful candidate will work closely with experienced geotechnical researchers who will provide the necessary domain expertise.

Beyond specific technical qualifications, we are particularly interested in candidates who can bridge disciplines. The successful candidate should be able to understand an engineering or scientific problem, identify where AI or computational methods can provide genuine added value, develop a rigorous solution, and transform that solution into a reusable capability for other researchers.

We therefore value scientific creativity, strong software-development practices, independence, collaborative ability and an enthusiasm for learning new areas.

The position is particularly suited to someone who is excited by the opportunity to help shape a new research direction at the intersection of Artificial Intelligence, mechanics, experimentation and geotechnical engineering.

We offer

• The opportunity to help shape a new research direction at the intersection of Artificial Intelligence, computational mechanics, experimentation and geotechnical engineering.
• Considerable freedom to identify research opportunities and contribute to the development of new methods as AI technologies and Scientific AI continue to evolve.
• The opportunity to work across scientific research, computational engineering, numerical simulations, experimental research and data science.
• Close collaboration with PhD students, postdoctoral researchers and senior scientists in an interdisciplinary research environment.
• The opportunity to develop reusable AI-enabled tools and workflows that strengthen research capabilities across the group.

Working, teaching and research at ETH Zurich

We value diversity and sustainability

In line with our values, ETH Zurich encourages an inclusive culture. We promote equality of opportunity, value diversity and nurture a working and learning environment in which the rights and dignity of all our staff and students are respected. Visit our Equal Opportunities and Diversity website to find out how we ensure a fair and open environment that allows everyone to grow and flourish. Sustainability is a core value for us – we are consistently working towards a climate-neutral future.

Curious? So are we.

We look forward to receiving your online application with the following documents:

• CV
• Motivation letter
• Diploma
• Reference Letters

Further information about the Chair of Geotechnical Engineering can be found on our website. Questions regarding the position should be directed to Dr. Alexandru Marin, Tel. +41 44 633 7370 or alexandru.marin@igt.baug.ethz.ch (no applications).

Please note that we exclusively accept applications submitted through our online application portal. Applications via email or postal services will not be considered.

We would like to point out that the pre-selection is carried out by the responsible recruiters and not by artificial intelligence.

About ETH Zürich

ETH Zurich is one of the world’s leading universities specialising in science and technology. We are renowned for our excellent education, cutting-edge fundamental research and direct transfer of new knowledge into society. Over 30,000 people from more than 120 countries find our university to be a place that promotes independent thinking and an environment that inspires excellence. Located in the heart of Europe, yet forging connections all over the world, we work together to develop solutions for the global challenges of today and tomorrow.