Assignments: With the help of the VirtUs team and under the supervision of Julien Pettré, the recruited person will be tasked with developing machine learning approaches capable of automatically modelling crowd dynamics from real-world field data. The central objective is to demonstrate that a learning-based model can capture the variety of crowd dynamics observed across different sites and situations — a challenge that remains largely unexplored in the field. The expected outcome is a new class of crowd simulation models that can automatically adapt to a specific crowd dynamic, as opposed to the universal, simplified rules used by current simulators.
For a better knowledge of the proposed research subject: A state of the art, bibliography and scientific references are available on the VirtUs team website: https://www.inria.fr/en/virtus
Collaboration: The recruited person will work in close connection with the first postdoctoral researcher of the FOUL-X project, who is responsible for building the field dataset that will serve as the primary input for the modelling work. The postdoc will also interact regularly with a PhD student of the team developing the pedestrian tracking pipeline, whose outputs feed directly into the learning process. This close collaboration ensures that modelling choices are informed by the nature and constraints of the available data, and reciprocally, that data acquisition is guided by the requirements of the learning approaches.
Responsibilities: The person recruited is responsible for the design, implementation and evaluation of machine learning models for crowd dynamics, working with the dataset progressively built during the project. The recruited person will take initiatives in exploring a range of modelling paradigms — including generative models, imitation learning, or physics-informed approaches — and will contribute to defining evaluation metrics adapted to the specific challenge of assessing the diversity of learned crowd dynamics.
Steering/Management: The person recruited will be in charge of the modelling and learning activities of the FOUL-X project, from the initial design of data representations and learning architectures to the evaluation and dissemination of results at major scientific venues.
