Student Research Assistant in Bridge Monitoring, Computer Vision & Data Science

The main tasks include:

Multi-modal data preprocessing: synchronising measurements from different sensing systems, identifying and cleaning faulty data, and improving data organisation and storage.

Traffic video analysis: applying deep-learning-based computer vision methods for vehicle detection and tracking; manually labelling data and fine-tuning models where needed; and using camera calibration and geometric transformation to determine vehicle locations and trajectories on the bridge.

Bridge Weigh-in-Motion analysis: fusing vehicle trajectories with bridge acceleration, strain and displacement measurements to estimate traffic loads.

Structural monitoring: analysing structural and environmental measurements to extract key performance indicators and investigate changes in bridge behaviour and structural condition.

Integrated condition assessment: combining identified traffic loads with structural response indicators to support more comprehensive assessment of bridge performance and condition.

Depending on the candidate’s background and interests, there will also be opportunities to develop and test new methods.

The Chair of Structural Mechanics & Monitoring, led by Prof. Eleni Chatzi, conducts research at the intersection of structural engineering, sensing, data science, and artificial intelligence for infrastructure monitoring.

The SwissBWIM project combines bridge structural monitoring and traffic monitoring data to identify vehicle loads and assess the structural condition and performance of highway bridges. The project develops robust and cost-efficient methods for bridge load identification and condition assessment based on the Bridge Weigh-in-Motion (B-WIM) concept. Monitoring-system installations have been completed on several highway bridges in Switzerland, and large-scale, multi-modal datasets are being continuously collected, including bridge acceleration, strain and displacement measurements, environmental data, and traffic videos.

The Student Research Assistant will work with an analysis framework developed by the Chair to process and integrate these datasets. The Chair has already developed methods required for most of the tasks. The role offers close supervision and a collaborative and international research environment within the Chair of Structural Mechanics & Monitoring.

• Currently enrolled at a university in Zurich or the nearby area.

• Background in civil/structural engineering, mechanical engineering, data science, computer science, or a related field.

• Experience or strong interest in two or more of the following: bridge engineering, structural dynamics, or system identification; sensing systems and signal processing; data analysis and machine learning; computer vision, particularly object detection, tracking, or localisation.

• Familiar with Python programming. Experience with MATLAB and Linux is an advantage.

• Can dedicate 15 hours per week to the project.

• Enjoy working on real-world engineering problems and developing practical, data-driven solutions.

• Proficient in English. The working language of the group is English.

• Prior experience in all listed areas is not expected; applicants with a strong foundation in two or more areas and motivation to learn the others are particularly welcome.