Offre en lien avec l’Action/le Réseau : – — –/Innovation
Laboratoire/Entreprise : LICIT – Univ. Gustave Eiffel
Durée : 12
Contact : angelo.furno@univ-eiffel.fr
Date limite de publication : 01/12/2020
Contexte :
We are looking for an enthusiastic Postdoctoral candidate to carry out research in the context of the ANR-funded DISCRET projet (French title: Démonstrateur d’ Identification de Situations Critiques via la Remontée de données multisources pour l’alErte en Temps-réel; English translation: Prototype for the identification of Critical Situations via multi-source data for real-time alert).
The goal of the project is to demonstrate the possibility to detect and locate, in real-time, unusual or critical situations in urban areas (e.g., attacks, fires, sudden weather-related events, etc.), based on the analysis of mobile phone probe network data. This detection will be complemented with information extracted from social networks (i.e., Twitter in the context of the project) and other sources of contextual data.
Several recent research works have shown that major events induce locally significant modifications of the amount and nature of cellular network communications [1]. These anomalies, typically concomitant with the unusual event, may be detected and located based on the network of cell phone antennas and the associated user-generated traffic information. The early detection and localization of the events allow for a more effective retrieval of information from the social networks. That permits to provide elements of description and context for the detected event and, therefore, to increase the confidence and the amount of information conveyed by the population via channels that are not explicitly conceived for alerting purposes.
A prototype of a warning platform for security and emergency operators will be implemented, tested and demonstrated as part of the whole project. The final prototype is expected to offer TRL 6 solutions by the end of the project that could be subsequently industrialized and operated by 2023, in the context of the Olympic Games that will be held in Paris in 2024.
The subject is at the interface between machine learning, big data processing, networking and transportation.
Sujet :
The Postdoc will have the unique opportunity to work on large-scale, already available mobile phone datasets, collected by the Orange French network provider, consisting in 2G, 3G and 4G network probe data, as well as more traditional Call Detail Records (CDR).
Additionally, novel highly-detailed datasets on the usages of Internet mobile phone apps from mobile phone users will be specifically collected in the framework of the project, as well as detailed information on the nature, occurrence and location of possible incidents during the observed events.
In a first phase, the activity of the postdoctoral candidate will consist in analyzing the collected data and extracting, via machine learning techniques and previous work from the team [2, 3], spatio-temporal fine-grained signatures of the typical network activity (aggregated at the antenna level) with different temporal resolution (5, 10, 60 minutes).
In a second phase, the postdoctoral fellow is expected to explore and define efficient classification techniques [4, 5] for the inference of atypical situations (increase in the volume of the communication and consumption activity of certain services, sudden growth of mobility-related events, change of signal shape, etc.) compared to prototypical mobile phone signatures as identified from phase 1.
This second phase will also focus on the creation of a process for periodic updating of signatures in order to adapt them to changes in the actual communication activity at certain places of interest.
Profil du candidat :
Data Science, Signal Processing, Machine Learning
Formation et compétences requises :
Anomaly Detection, Unsupervised/Supervised Learning, Statistical Learning, Signal processing
Adresse d’emploi :
Lyon, France (LICIT, UNIVERSITÉ GUSTAVE EIFFEL/ENTPE): https://goo.gl/maps/K19HBR4ETZ92
Document attaché : 202007221133_Call for postdoc – DISCRET.pdf