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

1000scholars; Rennes; On-site; EUR 40,000 - 48,000; Full time

Job summary

VirtUs team at the Inria Centre in Rennes invites applications for a postdoctoral position to develop ML models that learn crowd dynamics from real-world field data. Under supervision, you will design learning-based models including generative or imitation learning to capture diverse crowd behaviors across sites.

This project aims to deliver adaptive crowd simulators and contribute to data-driven methods in pedestrian dynamics. Collaboration with data acquisition teams is required.

Qualifications

• PhD in computer science or a related field.
• Strong background in deep learning and ML for dynamic systems.
• Experience with Python and PyTorch.
• Experience in trajectory prediction or human behaviour analysis.
• Ability to work in interdisciplinary team.

Responsibilities

• Design, implement and evaluate ML models for crowd dynamics.
• Work with field data and multiple acquisition sites.
• Explore generative models, imitation learning, and physics-informed approaches.
• Define evaluation metrics for diversity of learned dynamics.
• Disseminate results at major scientific venues.

Skills

Deep learning

Python / PyTorch

Trajectory prediction

Human behaviour analysis

Education

PhD in Computer Science or related field

Tools

PyTorch

C++

Job description

Context

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.

Assignment

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.

Main activities

1. 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
2. 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)

Skills

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