This project combines classical structural analysis with modern data-based and computational methods (AI & FEM) to advance the structural assessment of bridges under special road transports.

• A fully funded doctoral position at the Chair of Structural Engineering - Concrete Structures and Bridge Design (Prof. Walter Kaufmann), ETH Zurich
• In collaboration with the Computational Design Lab (Prof. B. Bickel), the ETH AI Center and Design++
• With direct practical relevance for Swiss infrastructure and the Swiss Federal Roads Office (ASTRA) as industry partner 

The Swiss Federal Roads Office (ASTRA) processes more than 20,000 applications for special road transports every year — vehicles or vehicle combinations that exceed the legal limits. Roughly half of these applications require a structural assessment of the bridges along the route. Given the high number of applications and the diversity of transport configurations and bridge inventories that need to be considered, this manual assessment is correspondingly time-intensive. This research project explores how methods from Artificial Intelligence (AI) and automation can complement and further develop the structural assessment of existing bridges under heavy transportation loads. Starting from established engineering practice, we investigate:

• Which parametric models and which representations of the most common bridge types allow us to assess structural safety and serviceability efficiently for special road transports?
• How can simulation data and bridge inventory data be combined so that data-driven models remain mechanically consistent, reliable, and interpretable?
• How can the influence of recurring heavy transports on structural damage and therefore on the durability of the structure be meaningfully captured in such models?

• Independent research exploring how methods from AI and automation can complement and further develop the structural assessment of existing bridges under heavy transportation loads
• Regular exchange with the industry partner ASTRA (Swiss Federal Roads Office) to ensure practical relevance
• Scientific writing of a doctoral thesis as well as peer-reviewed journal and conference publications, with the opportunity to attend leading international conferences in the field
• Support of teaching activities at the Chair & co-supervision of Bachelor's and Master's theses within the thematic scope of the position
• Completion of 12 course credits in the research field as part of the doctoral programme

• You enjoy working at the intersection of classical structural engineering and modern data- and computer-aided methods
• You hold a Master's degree in Civil Engineering, preferably with a specialisation in Structural Engineering
• Ideally you also have experience in scripting (Python) and AI/ Machine Learning / Data Science
• You work independently and with high initiative
• You communicate your work clearly in English, both in writing and in exchange with academic and industry partners (German is a plus)

• Supervision by Prof. Walter Kaufmann and Dr. Sophia Kuhn (Postdoc, computational design lab).
• As part of a chair associated with the ETH AI Center, the opportunity to become an associated researcher and join its community
• Additionally, access to the community of the Center for Augmented Computational Design in Architecture, Engineering and Construction (Design++)
• A range of career development programmes, with mentors from both industry and academia
• A great working environment and a supportive group, according to former doctoral students

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

• CV
• Motivation letter
• Transcripts / academic records

Applications are reviewed on a rolling basis until the position is filled. Start date by arrangement (no later than October 2026).

Further information about the Group can be found on our Website. Questions regarding the position should be directed to Sophia Kuhn, [email protected] (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.