Research Engineer F/M — Crowd data acquisition, processing and modelling

Inria; Rennes; Sur place; EUR 40 000 - 70 000; Plein temps

Avantages offerts par ce poste

Partial transport reimbursement

Annual leave + RTT days

Teleworking after 6 months

Professional equipment access

Social, cultural and sports events

Access to vocational training

Social security coverage

Résumé du poste

Inria Rennes Centre seeks a Research Engineer to support the FOUL-X project on crowd data acquisition, processing and modelling. You will help build and maintain the end-to-end experimental and computational pipeline, integrating video processing, trajectory extraction and data-driven modelling within a collaborative research environment.

You will work with postdoctoral researchers, contribute to software robustness, tooling, data management and reproducibility, and help turn research ideas into

Qualifications

• Strong programming skills, particularly in Python.
• Experience in scientific computing, data processing, computer vision, machine learning, simulation.
• Ability to manipulate and process large datasets.
• Experience developing robust research or scientific software.
• Familiarity with standard collaborative software development tools such as Git.

Responsabilités

• Support field data acquisition, video processing and trajectory extraction.
• Develop tools for dataset construction, exploration and visualization.
• Contribute to data-driven modelling and integration with crowd simulation software.
• Ensure reproducibility, robustness and documentation of software and datasets.

Connaissances

Python

Data processing

Computer vision

Machine learning

Scientific computing

Software development

Git

Outils

OpenCV

NumPy

SciPy

Pandas

PyTorch

Git

Description du poste

Research Engineer F/M — Crowd data acquisition, processing and modelling

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 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.

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;

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.

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 as density, velocity, flow, interpersonal distances and other collective quantities.

Support the implementation of new metrics developed by the postdoctoral researchers to compare crowd dynamics across sites.

Contribute to preparing datasets for public dissemination and long-term reuse.

Activity 4 — Support for data-driven modelling

Prepare and transform trajectory and environmental data for machine-learning pipelines.

Implement data loaders, preprocessing tools and evaluation scripts.

Support the deployment and testing of machine-learning models developed by the modelling postdoc.

Run training and evaluation experiments on local or remote computing resources.

Develop interfaces between learned models and the crowd simulation software used by the team.

Contribute to benchmarking, visualisation and quantitative comparison of simulation results.

Activity 5 — Software integration and reproducibility

Maintain and document the software components developed throughout the project.

Contribute to version control, testing and reproducible experiment workflows.

Facilitate integration between tracking, dataset analysis, machine learning and crowd simulation software.

Produce technical documentation enabling the tools to be reused by future team members and by the scientific community.

Participate in the preparation of open-source software and open datasets resulting from the project.

Technical skills — required

Strong programming skills, particularly in Python.

Experience in scientific computing, data processing, computer vision, machine learning, simulation, or a closely related field.

Ability to manipulate and process large datasets.

Experience developing robust research or scientific software.

Familiarity with standard collaborative software development tools such as Git.

Ability to rapidly understand and integrate existing software components.

Technical skills — highly appreciated

Candidates are not expected to already master all of the following. Experience in several of these areas would be particularly valuable:

Image and video processing.

Object or pedestrian tracking.

Camera calibration and multi-camera systems.

Computer vision libraries such as OpenCV.

Machine learning and deep learning frameworks such as PyTorch.

Data analysis using NumPy, SciPy, Pandas or equivalent tools.

Simulation or computer graphics.

Processing and analysis of human trajectories.

GPU computing and management of machine-learning experiments.

Scientific visualisation.

Linux environments and scripting.

Experience with field experimentation and acquisition hardware.

Experience preparing research datasets for public release.

Languages

English: sufficient level to work in an international research environment and read technical documentation.

French: appreciated, particularly for field acquisition activities in France, but not mandatory.

Relational skills

Strong ability to work in a collaborative research environment.

Taste for teamwork and for supporting the work of other researchers.

Ability to interact with researchers with different backgrounds, from experimentation and computer vision to machine learning and simulation.

Autonomy in solving technical problems.

Organisational skills and ability to manage several technical tasks in parallel.

Adaptability and willingness to work on different parts of a research pipeline according to project needs.

Curiosity and interest in understanding the scientific objectives behind technical developments.

Avantages

• 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

Starting from €2,695 gross per month, based on your experience

The ideal candidate is a versatile research engineer who enjoys making complex experimental and computational systems work in practice.

Rather than being a specialist restricted to a single technology, we are looking for someone comfortable moving between different components of a research project: preparing an acquisition campaign one week, improving a video-processing pipeline the next, analysing trajectory data, and then helping connect these data to a machine-learning or simulation pipeline.

A strong background in computer science or engineering is expected, together with solid programming skills. Candidates may come from computer vision, machine learning, robotics, computer graphics, data science, scientific computing, or related domains.

It is not necessary to already have expertise in crowd simulation. More important is the ability to understand existing methods, implement and adapt technical solutions, analyse problems rigorously, and work closely with researchers.

The position requires a genuine taste for teamwork. The engineer will work in direct support of two postdoctoral researchers whose activities are strongly interconnected. A key part of the role will be to help make these interactions effective by ensuring that data, software and experimental tools can move smoothly from one stage of the project to another.

We particularly value candidates who are pragmatic, curious and autonomous, who enjoy solving unexpected technical problems, and who care about producing clean, documented and reusable research software.