PhD Position – RUN2GETHER – Dynamic Interaction Between Runners and Infrastructure KU Leuven in United Kingdom

KU Leuven is offering a PhD position within the interdisciplinary RUN2GETHER project, which studies how runners, social identity, technology, and urban infrastructure interact in real-world settings. The project uses running groups and events as living laboratories to explore collective movement, biomechanics, human motion analysis, structural engineering, and machine-learning-enabled data collection.

This specific PhD focuses on the influence of collective running behaviour on the dynamic loads exerted by runners on civil infrastructure. The research will combine controlled experiments with measurements collected during real-world running events. A key goal is to understand the dynamic interaction between runners and infrastructure, and to formulate recommendations for representative loading scenarios that can support infrastructure design. The project will also explore whether runners can be used as “human sensors” to assess infrastructure quality.

The work is highly interdisciplinary and connects structural mechanics, biomechanics, signal analysis, wearable sensing, and computer vision. It is positioned within KU Leuven and involves collaboration with other researchers in engineering and computer science. The offer includes conducting scientific research on the dynamic running load and its impact on the dynamic performance of footbridges, supervising master’s thesis projects in this domain, teaching assistance depending on the candidate’s profile, and administrative and technical support for the Structural Mechanics Research Group and the Faculty of Engineering Technology.

Applicants should hold a Master’s degree in Civil Engineering or a related field closely aligned with the PhD topic. A good knowledge of or strong interest in biomechanics and human motion analysis is important. Experience or interest in experimental data collection, such as structural vibrations and human body motion, and in signal analysis is considered a strong asset. The ideal candidate is creative, independent, a team player, and comfortable working across disciplines. An excellent CV is expected, and applicants should be willing to prepare a personal funding application with supervisor support. Strong communication skills, responsibility, and a willingness to take on teaching tasks are also highlighted. Candidates who have not yet graduated but have a strong curriculum vitae by the deadline are encouraged to apply.

The position is based in Leuven, Belgium at KU Leuven. The application deadline is 2026-08-14.