- A curriculum vitae;
- Transcript of records (marks) up to the Master degree;
- The Phd Thesis manuscript;
- A research letter discussing a personal vision of the position, wrt references proposed in the call (see below)
- At least, one recommendation letter and a list of references to contact.
A PhD on a computer science / data analysis / machine learning / applied maths / statistics topic is required.
Apologies for cross-posting.
Title: Improving security for Paris 2024 Olympique Games: discovering anomalous urban situations via real-time analysis of mobile phone data
Postdoctoral position (12 months)
Primary research field: Computer Science - Machine Learning - Data Science
Secondary research field: Statistics - Signal Processing
Start date: September 2020
Net salary: ~2,000-2300 Euros per month. Some additional income can be earned by teaching.
Academic and industrial professional development including travel support.
Interaction with world-renowned external board members and speakers
Travel grant for attending conferences and workshops.
Research Center: University Gustave Eiffel - Campus Lyon, LICIT laboratory
(25 avenue Francois Mitterand, Bron, France).
Angelo FURNO (Researcher), LICIT, University of Lyon, ENTPE, UNIVERSITÉ GUSTAVE EIFFEL-COSYS
Nour-Eddin EL FAOUZI (Lab Director), LICIT, University
of Lyon, ENTPE, UNIVERSITÉ GUSTAVE EIFFEL-COSYS
Marco FIORE (Researcher), IMDEA, Madrid, Spain
Zbigniew SMOREDA (Researcher) and Tamara TOSIC (Researcher), Orange, France
Eric GAUME (Lab Director), UNIVERSITÉ GUSTAVE
Postdoc project description================
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
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.
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].
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.).
From a methodological point of view, the main challenge is to develop a classification method that can work in real time.
To achieve this goal, a method combining artificial intelligence (AI) and statistical learning will be designed and implemented
(neural networks-based approaches, curve classification method, kernel method or generative method).
As the volume of data to be analyzed is expected to be significant, the detection of anomalies and the periodic update
of local signatures will have to be carried out as close as possible to the source of the data flows so as to minimize network
load and latency (Mobile Edge Computing).
From a technology perspective, the computational complexity of data mining requires the use of appropriate solutions
for real-time and scalable implementation.
As part of the project we will explore the use of Apache Kafka, TensorFlow and Apache Flink open source platforms
to meet this criterion.
It is expected that the successful candidate will contribute to top-tier computer networks, self-adaptive distributed systems,
big data and machine-learning conferences and journals (IEEE INFOCOM, IEEE ICDM, ACM SIGKDD, IEEE Big Data,
IEEE Transactions on Autonomous and Adaptive Systems, IEEE Intelligent Transportation Systems, etc.).
We are ready to schedule a Skype interview to discuss more about the available position.
The postdoctoral position is expected to start in September/October 2020 at latest.
The link to the full call is available here:
 D. Naboulsi, M. Fiore, S. Ribot, and R. Stanica, “Large-scale mobile traffic analysis: a survey,” IEEE Communications Surveys & Tutorials, vol. 18, no. 1, pp. 124–161, 2015.
 A. Furno, M. Fiore and R. Stanica, "Joint spatial and temporal classification of mobile traffic demands," in INFOCOM 2017-IEEE Conference on Computer Communications, IEEE, 2017.
 A. Furno, M. Fiore, R. Stanica, C. Ziemlicki and Z. Smoreda, "A tale of ten cities: Characterizing signatures of mobile traffic in urban areas," IEEE Transactions on Mobile Computing, vol. 16, pp. 2682-2696, 2017.
 L. Fahrmeir, T. Kneib, S. Lang, B. Marx. “Regression : Models, Methods and Applications”. Berlin: Springer. p. 663, 2013.
 A. Ben-Aissa, N.-E. El Faouzi, and E. Lefevre, “Classification multisource via la théorie des fonctions de croyance: application à l’estimation du temps de parcours,” Revue de Statistique Appliquée, p. 17p, 2009.
 X. Wang, Y. Han, C. Wang, Q. Zhao, X. Chen, and M. Chen, “In-edge ai: Intelligentizing mobile edge computing, caching and communication by federated learning,” arXiv preprint arXiv:1809.07857, 2018.
 N. di Pietro, M. Merluzzi, E. C. Strinati, and S. Barbarossa, “Resilient design of 5g mobile-edge computing over intermittent mmwave links,” arXiv preprint arXiv:1901.01894, 2019.
Thank you in advance for your support.