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

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Offre n°2026-10560

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

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Le descriptif de l’offre ci-dessous est en Anglais

Type de contrat : CDD

Niveau de diplôme exigé : Thèse ou équivalent

Fonction : Post-Doctorant

A propos du centre ou de la direction fonctionnelle

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.

Contexte et atouts du poste

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.

Mission confiée

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.

Principales activités

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)

Compétences

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

Avantages

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

Rémunération

Monthly gross salary amounting to 2788 euros

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Informations générales

Thème/Domaine : Représentation et traitement des données et des connaissances

Statistiques (Big data) (BAP E)

Ville : Rennes

Centre Inria : Centre Inria de l'Université de Rennes

Date de prise de fonction souhaitée : 2027-01-01

Durée de contrat : 2 ans

Date limite pour postuler : 2026-12-31

Attention: Les candidatures doivent être déposées en ligne sur le site Inria. Le traitement des candidatures adressées par d'autres canaux n'est pas garanti.

Consignes pour postuler

Please submit online : your resume, cover letter and letters of recommendation eventually

Sécurité défense :

Ce poste est susceptible d’être affecté dans une zone à régime restrictif (ZRR), telle que définie dans le décret n°2011-1425 relatif à la protection du potentiel scientifique et technique de la nation (PPST). L’autorisation d’accès à une zone est délivrée par le chef d’établissement, après avis ministériel favorable, tel que défini dans l’arrêté du 03 juillet 2012, relatif à la PPST. Un avis ministériel défavorable pour un poste affecté dans une ZRR aurait pour conséquence l’annulation du recrutement.

Politique de recrutement :

Dans le cadre de sa politique diversité, tous les postes Inria sont accessibles aux personnes en situation de handicap.

Contacts

Équipe Inria : VIRTUS

Recruteur :

Pettre Julien /

L'essentiel pour réussir

The ideal candidate is driven by the scientific challenge of teaching a machine to reproduce the complexity of human crowd behaviour from real-world observations. They are comfortable navigating an open and exploratory research landscape, where the right modelling approach is not known in advance and where scientific creativity is as important as technical expertise.

A strong background in machine learning and a genuine interest in its application to the modelling of physical or social systems are the natural profile for this position. We also welcome candidates with a background in computer graphics or character animation who have developed expertise in data-driven approaches to motion modelling.

The position requires intellectual curiosity, the ability to work with limited and imperfect data, and a taste for bridging the gap between real-world observations and simulation. A collaborative mindset is essential, as the modelling work is tightly coupled to the data acquisition activities of PDoc 1 — the nature and quality of the available data will directly shape the modelling choices.

A propos d'Inria

Inria, l'institut national de recherche dans les sciences et technologies du numérique, est en appui de l’État pour les stratégies nationales de recherche et d’innovation du numérique en tant qu'Agence de programmes. Inria mène plus de 300 projets de recherche et d’innovation avec ses 3500 scientifiques, ingénieurs et personnels d’appui, en partenariat avec les universités et l’écosystème numérique (entreprises, entrepreneurs, acteurs publics). Ensemble, nous explorons des domaines clés comme l'intelligence artificielle, la cybersécurité, l’informatique quantique, le Cloud, la transformation numérique de la santé, les jumeaux numériques ou encore les technologies numériques pour la défense. Nous construisons des solutions concrètes telles que des logiciels, des startups technologiques, des partenariats avec les entreprises du tissu national et des formations de pointe. Notre objectif : l’impact scientifique, technologique et industriel au service de la souveraineté numérique de la France.

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