Post-Doctoral Research Visit F/M Learning crowd dynamics from real-world data

The Inria Centre at Rennes University is one of Inria's nine centres and has more than thirty research teams. The Inria Centre is a major and recognized player in the field of digital sciences. It is at the heart of a rich R&D and innovation ecosystem: highly innovative PMEs, large industrial groups, competitiveness clusters, research and higher education players, laboratories of excellence, technological research institute, etc.

The VirtUs team at the Inria Centre at the University of Rennes is internationally recognized for its work in crowd simulation and the study of collective human behaviour. This postdoctoral position is part of the FOUL-X project (Programme Inria Quadrant), which aims to develop a new generation of crowd simulators capable of automatically adapting to the specific dynamics of a given environment or situation.

Current crowd simulation models rely on simplified, universal rules that fail to capture the diversity of behaviours observed in real-world settings. FOUL-X challenges this paradigm by exploring data-driven approaches that learn crowd dynamics directly from field observations. This requires addressing open scientific questions on how to represent crowd data, which learning architectures are best suited to capture collective behaviours, and how to evaluate the realism of learned simulations.

This postdoc focuses on the development of machine learning models for crowd dynamics, working in close collaboration with the data acquisition activities of the project. The work will span the full pipeline from data representation to model learning and evaluation, with the ultimate goal of demonstrating an adaptive crowd simulator built on real-world data.

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.

Phase 1 — Architecture design and preliminary learning (months 1–6)

• Conduct a targeted review of existing approaches for data-driven crowd dynamics modelling, covering trajectory prediction, generative models, imitation learning, and physics-informed approaches
• Define crowd data representations suited to machine learning, combining individual (positions, velocities), collective (density, flow), and environmental (obstacles, spatial layout) information
• Select and implement the most promising learning architecture for crowd dynamics modelling, based on pre-existing datasets available in the team
• Validate the technical functioning of the learning pipeline and establish baseline performance metrics

Phase 2 — Learning diverse crowd dynamics from FOUL-X data (months 7–24)

• Develop and iteratively refine machine learning models for crowd dynamics using the dataset progressively built by PDoc 1 across multiple acquisition sites
• Address the challenges of learning from limited and partially observable real-world data, exploring techniques such as transfer learning, data augmentation, and weak supervision
• Demonstrate the capacity of the models to capture and distinguish diverse crowd dynamics, as observed across different sites, populations, and spatial configurations
• Contribute to the definition of evaluation metrics adapted to the assessment of diversity in learned crowd dynamics, in collaboration with PDoc 1
• Disseminate results at major scientific venues (IEEE CVPR, ACM SIGGRAPH, PED 2027)

Technical skills (required):

• Deep learning, in particular generative models and/or imitation learning
• Programming in Python (PyTorch or equivalent)
• Experience in trajectory prediction, motion modelling, or human behaviour analysis

Technical skills (a plus):

• Background in crowd simulation or collective behaviour modelling
• Experience with C++ for simulation development
• Familiarity with evaluation metrics for trajectory prediction (ADE, FDE)

Languages:

• English (required for scientific dissemination)

Relational skills:

• Autonomy and scientific initiative in an exploratory research context
• Ability to work in a collaborative and interdisciplinary environment
• Good communication skills for regular interactions with the data acquisition team

• Subsidized meals
• Partial reimbursement of public transport costs
• Leave: 7 weeks of annual leave + 10 extra days off due to RTT (statutory reduction in working hours) + possibility of exceptional leave (sick children, moving home, etc.)
• Possibility of teleworking (after 6 months of employment) and flexible organization of working hours
• Professional equipment available (videoconferencing, loan of computer equipment, etc.)
• Social, cultural and sports events and activities
• Access to vocational training
• Social security coverage

Monthly gross salary amounting to 2788 euros