PIONEER-TOPIC#2025: PIONEER (Paris + 12M in Lisbon) PhD call: Risk assessment of electric two-w[...]

European Commission; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

Une thèse dans le domaine de l'Intelligence Artificielle est proposée par la Commission Européenne pour explorer la sécurité routière des deux-roues électriques. Le candidat sera formé pour mener des recherches interdisciplinaires, avec un séjour de mobilité à Lisbonne, et aura accès à des infrastructures de recherche de haut niveau. Cette opportunité convient aux candidats ayant un fort intérêt pour les systèmes de mobilité et une solide formation en science informatique.

Prestations

25 jours de congés annuels payés

Accès à la sécurité sociale et à l'assurance santé

Participation à des ateliers internationaux

Qualifications

• Diplôme de Master requis dans un domaine pertinent (IA, informatique).
• Capacité et volonté de travailler en mobilité internationale.

Responsabilités

• Développer une méthodologie pour étudier le comportement des utilisateurs de deux-roues électriques.
• Exploiter des données de conduite pour détecter des événements critiques.

Connaissances

Intelligence Artificielle

Apprentissage Machine

Vision par Ordinateur

Programmation Python

Programmation C++

Formation

Master en Sciences Informatiques

Master en Mathématiques Appliquées

Description du poste

Organisation/Company Université Gustave Eiffel Department SATIE Research Field Computer science » Informatics Mathematics » Applied mathematics Engineering » Computer engineering Researcher Profile First Stage Researcher (R1) Positions PhD Positions Country France Application Deadline 31 Jul 2025 - 23:59 (Europe/Paris) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Dec 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

Generative artificial intelligence (AI) offers many promises, while raising questions about its usefulness and performance in its many applications. This thesis focuses on the contribution of generative AI to road safety, with a particular emphasis on the safety of electric two-wheelers. In France, the cycling population continues to grow, accompanied by a significant increase in trips (+13% in 2022 compared to 2021 and +41% compared to 2018). However, this growth is accompanied by a rise in the number of cyclists killed (+31% between 2022 and 2019). Electrically-assisted bicycles (EABs) are increasingly concerned, with a 72% increase in fatalities linked to this category between 2019 (25 fatalities) and 2022 (43 fatalities). With regard to mopeds, the increase in the number of motorized two-wheelers is leading to a rise in injuries among users, particularly among young people (aged 14-17), who do not protect themselves sufficiently against the risks inherent in driving this type of vehicle (ONISR, 2020).

Despite this increase in the number of users, the body of scientific data on these practices and this population remains limited. A review of the literature reveals a lack of in-depth knowledge about their behaviors and interactions with other users in real-life situations, particularly vulnerable users.

The main aim of this thesis is to develop a methodology for transport researchers to study the behavior of electric two-wheeler (VAE and electric scooter) riders in high-risk situations. These situations often result from inappropriate actions, influenced by various factors: driving experience, emotional state (fatigue, frustration), vehicle dynamics, road infrastructure and environmental conditions (weather, road conditions, traffic density). Detecting these high-risk situations is a major challenge, particularly for two-wheelers, given the instability of the vehicle and the fragility of the driver, requiring constant anticipation of changes in the driving environment.

This thesis follows on from other work focusing on the detection of critical events in two-wheeler driving. Previous theses, such as those by Attal (2015) and Diop (2022), have explored approaches based on unsupervised machine learning and anomaly detection to identify critical events from data on vehicle dynamics (accelerometers, gyrometers) and context (GPS). As part of the 2RLS project, funded by the DSR, a study has been carried out to improve knowledge of users of self-service electric bikes and scooters, their practices and the associated risks. An experiment carried out in Paris with 19 participants over a 10 km course collected heterogeneous data: 360° videos, audio recordings, accelerometric and gyroscopic measurements, and GPS data.

The main objective of this thesis is to exploit this mass of data by drawing on recent advances in artificial intelligence, particularly generative AI, to detect critical events during the riding of electric two-wheelers. A methodology integrating language and vision models (LLM and VLM) will be developed to analyze the driving environment captured by 360° cameras. This methodology will combine advanced image and video processing techniques, such as a video token sparsification method, to optimize the processing of the large quantities of data generated. VLM models will enable accurate interpretation of visual scenes and object recognition, while LLMs will analyze context and understand the complex interactions between different elements of the road environment. The information extracted by VLMs, coupled with vehicle dynamic data, will enable us to assess the risks associated with interactions between road users. For example, heavy traffic and wet pavement represent an increased risk of sudden braking or accident. The presence of an obstacle on the road increases the risk of collision, depending on traffic density.

• Li, J., Li, D., Xiong, C., & Hoi, S. (2022, June). Blip: Bootstrapping language-image pre-training for unified vision-language understanding and generation. In International conference on machine learning (pp. 12888-12900). PMLR.
• Khan, S., Naseer, M., Hayat, M., Zamir, S. W., Khan, F. S., & Shah, M. (2022). Transformers in vision: A survey. ACM computing surveys (CSUR), 54(10s), 1-41.
• L. H., Zhang, P., Zhang, H., Yang, J., Li, C., Zhong, Y., ... & Gao, J. (2022). Grounded languageimage pre-training. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (pp. 10965-10975).
• Zhang, J., Huang, J., Jin, S., & Lu, S. (2024). Vision-language models for vision tasks: A survey.

IEEE Transactions on Pattern Analysis and Machine Intelligence.

Where to apply

E-mail [e-mail]

Requirements

Research Field Computer science » Informatics Education Level Master Degree or equivalent

Skills/Qualifications

• Strong background in computer engineering and computer vision.
• Artificial Intelligence, Machine/Deep Learning, Data Mining
• Fluency in English (working language of the PhD)

Specific Requirements

• A Master’s degree (or equivalent) in one of the following fields:
  Applied Mathematics, Computer Science
• Proven interest or background in mobility systems
• Strong programming skills, preferably in Python, C++.
• Good command of English (written and spoken) – working language of the PhD.
• Ability to work independently while collaborating across institutions.
• Willingness and ability to spend research stays abroad, especially at University Institute of Lisbon ISCTE, during the PhD.

Languages ENGLISH Level Good

Research Field Computer science » ProgrammingEngineering » Computer engineeringMathematics » Applied mathematics

Additional Information

• High-quality doctoral training leading to a PhD degree delivered by Université Gustave Eiffel, under joint supervision with University Institute of Lisbon ISCTE (PIONEER partner).
• Appointment for 36 months under a full-time doctoral contract.
• Employment under French labour law, including 35 hours per week, 25 days of annual paid leave and access to social security and health insurance
• Opportunity to work in a multidisciplinary international team, with guidance from experts in shared mobility, transportation resilience, and urban accessibility.
• Potential eligibility for the European Doctorate label, subject to conditions.
• Participation in international workshops, scientific conferences, and mobility to partner institutions in the EU.
• Access to laboratories and resources at both universities (including datasets, and computing facilities).

Eligibility criteria

• Master’s degree or equivalent (Bac+5) in one of the following fields:
  • Computer Science
  • Applied Mathematics
  • Machine and Deep Learning
  • Computer Vision
• Strong academic background and motivation to conduct interdisciplinary research
• Proficiency in English (spoken and written)
• Candidates must meet mobility rules under the PIONEER framework

Selection process

The thesis will take place under the PIONEER+ project, a European Universities Alliance, coordinated by université Gustave Eiffel.
Candidates pre-selected by the thesis supervisors will be invited to an interview in English before a panel composed of members of the PIONEER alliance between September 22nd and October 15th, 2025.

Additional comments

The doctoral candidate will conduct 12 months of research mobility at University Institute of Lisbon ISCTE during the PhD program.