Subject : Monitoring mobility patterns: detection of punctual anomalies and long-lasting disruptions

Contract duration: 12 months with possible extension to 18 months

The objective of the post-doctorate is to create innovative methods that identify significant events for local authorities, particularly those not explained by predefined factors such as weather conditions. The approach will focus on two key dimensions: spatial and temporal, and it will be designed to apply to various modes of transportation, including public transportation, bicycles, cars, or the total sum of flows, whose data will be provided. Additionally, the framework aims to detect long-term changes in mobility behavior.
Given the absence of a comprehensive database of all potential traffic disturbances, the methodology will be unsupervised. However, the approaches can be validated using a defined set of known cases. Various methodologies can be employed, including statistical analysis, similarity-based techniques, or pattern mining.

The candidate will join an experienced research team in mobility analysis and traffic estimation with close connections to local authorities.

They will furthermore be part of the Mob Sci-Dat Factory project, in partnership with CEREMA, IGN-ENSG, INRIA, and Université Gustave Eiffel, which aims to improve methods for collecting, processing, and analyzing heterogeneous mobility data.

• Proficiency in Python is required.
• The candidate must also demonstrate excellent written and verbal communication skills in English. Knowledge of French is a plus.

The candidate must hold a PhD in statistics, machine learning, transportation science or related discipline, with experience in anomaly detection.

For further information and to apply, please contact:

• Alexandre Lanvin, alexandre.lanvin@ifpen.fr

Department of Control, Signal and System, IFP Energies Nouvelles