Research Engineer – — Crowd data acquisition, processing and modelling

Type de contrat : CDD Contexte et atouts du poste The VirtUs team at the Inria Centre at Rennes University is internationally recognized for its work in crowd simulation and the study of collective human behaviour. This position is part of the FOUL-X project (Programme Inria Quadrant), which aims to develop a new generation of crowd simulators capable of capturing and reproducing the diversity of crowd dynamics observed in real-world public spaces. The project combines two closely connected research directions. The first focuses on the acquisition and processing of real-world crowd data, from field video capture to individual trajectory extraction and the construction of a large-scale open dataset. The second focuses on learning crowd dynamics directly from these observations in order to develop adaptive, data-driven crowd simulation models. Two postdoctoral researchers will lead these complementary scientific activities. The recruited research engineer will work in close support of this postdoctoral team, contributing to the technical implementation, operation and integration of the complete experimental and computational pipeline. The position therefore offers a unique opportunity to work at the interface between real-world experimentation, computer vision and data processing, scientific software development, data analysis and machine learning. Mission confiée With the support of the VirtUs team and under the supervision of Julien Pettré, the recruited person will provide technical and experimental support to the two postdoctoral researchers involved in the FOUL-X project. The main objective is to ensure that the different components of the project — field data acquisition, video processing, trajectory extraction, dataset construction, quantitative analysis and data-driven modelling — can be efficiently deployed, connected and maintained throughout the project. The engineer will not be responsible for defining the scientific directions of the two postdoctoral projects, but will play a central role in turning research ideas into robust operational pipelines, supporting experiments, implementing tools, processing data, and facilitating interactions between the data acquisition and modelling activities. Collaboration: The recruited person will work on a daily basis with: • the postdoctoral researcher responsible for real-world crowd data acquisition, dataset construction and crowd dynamics characterisation; • the postdoctoral researcher responsible for machine-learning-based crowd modelling; • a PhD student developing video-based pedestrian tracking methods; • other researchers, engineers and students of the VirtUs team involved in crowd simulation and analysis. This position is therefore particularly suited to someone who enjoys working collaboratively and contributing to several interconnected research activities rather than focusing on a single isolated technical task. Responsibilities: The recruited person will contribute to the implementation, testing and maintenance of the technical infrastructure required by the project. Depending on project needs and on the candidate's expertise, this will include field acquisition systems, video and trajectory processing pipelines, dataset management and visualisation tools, simulation software, and machine-learning pipelines. The recruited person will also contribute to ensuring the reproducibility, robustness and documentation of the software and datasets developed within the project. Principales activités Activity 1 — Support for field data acquisition • Contribute to the preparation and deployment of video acquisition systems for crowd observation campaigns. • Prepare, test and maintain cameras, computing equipment and associated acquisition tools. • Participate in field acquisition campaigns at several sites in France. • Develop or adapt tools for camera calibration, acquisition monitoring and data transfer. • Contribute to checking data quality during and immediately after acquisition campaigns. • Assist with the technical implementation of data anonymisation and GDPR-related requirements. Activity 2 — Video processing and trajectory extraction • Support the deployment and operation of the video-based pedestrian tracking pipeline developed within the team. • Develop scripts and tools for preprocessing large volumes of video data. • Contribute to camera calibration, geometric reconstruction, tracking validation and correction of extracted trajectories. • Automate data-processing workflows where possible. • Analyse pipeline failures and contribute to improving robustness across acquisition conditions. • Develop tools for visual inspection and quality control of trajectory data. Activity 3 — Dataset construction and quantitative analysis • Contribute to structuring, cleaning, validating and documenting the FOUL-X crowd trajectory dataset. • Implement tools for dataset exploration and visualisation. • Compute standard crowd descriptors such