RUN2GETHER – PhD in AI and Computer Vision for Mapping Runners KU Leuven in United Kingdom

RUN2GETHER is an interdisciplinary KU Leuven PhD project at the intersection of human movement, social identity, technology, and urban infrastructure. The project treats running groups and running events as a living lab and investigates how collective running can contribute to healthier citizens and more sustainable cities.

The advertised doctorate focuses on the analysis of video and other real-world data captured during group runs and running events. Using AI-based techniques, the project will extract biomechanical variables such as pose, cadence, and synchrony from video, supported by on-body sensor data including IMU measurements. The research will combine controlled experiments with observations in authentic event settings, and will also involve the development of an interactive interface to efficiently collect subjective data from participants while they are running.

This vacancy is hosted by KU Leuven and linked to the EAVISE research group, with the main workplace at KU Leuven Campus De Nayer in Sint-Katelijne-Waver. The broader project brings together several disciplines, including biomechanics, social and sports psychology, civil/structural aspects of running infrastructure, and machine learning. The project aims to understand how shared social identity influences movement synchrony, physical loading, and interaction with infrastructure such as bridges and running facilities, while also contributing to citizen science and infrastructure monitoring.

Applicants should have a master's degree in industrial engineering sciences, electronics-ICT, or a related field. Strong interest or experience in digital signal processing, machine learning, and computer vision is expected, and knowledge of biomechanics and experimental data collection is a clear advantage. The position particularly suits candidates with a multidisciplinary mindset who are creative, self-driven, and comfortable working in an international and cross-disciplinary environment. Candidates should also be willing to take on teaching-related tasks and administrative or technical support activities within the research group and faculty.

The offer includes research on machine learning-based computer vision methods for measuring running-related parameters, development of a user interface for collecting participant feedback, supervision of master theses, teaching assistance, and support for activities within EAVISE and the Faculty of Industrial Engineering Sciences. Interested candidates can contact Prof. dr. ir. Patrick Vandewalle at patrick.vandewalle@kuleuven.be. Applications must be submitted through the KU Leuven recruitment portal before 2026-08-14 23:59 (CET).