Dynamic Interaction Between Runners and Infrastructure

There will be three PhD closely working together in this research project. The PhD research associated with this position focuses on the influence of collective running behaviour on the dynamic loads exerted by runners on civil infrastructure. This will be investigated through both controlled experiments and measurements conducted during real-world running events. In addition, the dynamic interaction between runners and the infrastructure will be studied. Recommendations will be formulated for representative loading scenarios to support the design of infrastructure. Finally, the project will investigate whether runners can be used as “human sensors” to assess the quality of the infrastructure., (Candidates who have not yet graduated but have a strong curriculum vitae by the application deadline are encouraged to apply.)

• Conducting scientific research in the field of structural mechanics, more specifically, the dynamic running load and its impact on the dynamic performance of footbridges.
• Supervising master thesis projects in this research domain.
• Providing teaching assistance in courses depending on the candidate’s profile.
• Providing administrative and technical support for activities within the Structural Mechanics Research Group, and the faculty of Engineering Technology., * Conducting scientific research in the field of structural mechanics, more specifically, the dynamic running load and its impact on the dynamic performance of footbridges.
• Supervising master thesis projects in this research domain.
• Providing teaching assistance in courses depending on the candidate’s profile.
• Providing administrative and technical support for activities within the Structural Mechanics Research Group, and the faculty of Engineering Technology.

• You have a Master’s degree in Civil Engineering, or a related field that aligns with the topic of the PhD research.
• You have a good knowledge of and/or a strong interest in biomechanics and human motion analysis. Knowledge of or a strong interest in collecting experimental data (e.g., structural vibrations and/or human body motion) and signal analysis are considered strong assets.
• You have a multidisciplinary mindset. You are creative, take initiative, and can work independently. You are also a team player and are looking forward to work closely with other researchers from different disciplines (engineering and computer science).
• You have an excellent CV and are willing to write a personal funding application with the assistance of the supervisors.
• You have communication skills and a sense of responsibility.
• You are willing to take up some teaching tasks., * You have a Master’s degree in Civil Engineering, or a related field that aligns with the topic of the PhD research.
• You have a good knowledge of and/or a strong interest in biomechanics and human motion analysis. Knowledge of or a strong interest in collecting experimental data (e.g., structural vibrations and/or human body motion) and signal analysis are considered strong assets.
• You have a multidisciplinary mindset. You are creative, take initiative, and can work independently. You are also a team player and are looking forward to work closely with other researchers from different disciplines (engineering and computer science).
• You have an excellent CV and are willing to write a personal funding application with the assistance of the supervisors.
• You have communication skills and a sense of responsibility.
• You are willing to take up some teaching tasks.

(Candidates who have not yet graduated but have a strong curriculum vitae by the application deadline are encouraged to apply.) Selection process

RUN2GETHER explores how collective running, one of the world’s most popular physical activities, can help build healthier citizens and more sustainable cities. Bringing together insights from biomechanics, from social and sport psychology, and from structural engineering stimulated by recent developments in machine learning, the project investigates how runners’ shared social identity can influence their movement synchrony, physical load, and interaction with the running infrastructures. Using cutting-edge computer vision, wearable sensors, and citizen science, RUN2GETHER will capture large-scale, real-world data during running events and group runs. These data will advance our understanding of how human collectives move together and how this can affect the performance and resilience of both individuals and of urban infrastructures such as bridges and running tracks. By engaging citizens as active contributors to science and running surface health monitoring, the project aims to transform running events into living laboratories for innovation in health, social cohesion, and sustainable city design.