[Apologies for Cross-Posting]
Please, consider sharing this call for postdoc among colleagues, post-docs and last-year
*** CONTEXT AND MAIN PURPOSE ***
Guaranteeing efficient, resilient and sustainable mobility in modern large cities is a
very challenging task, due to growing transport demand, climate changes, industrialization
and population increase.
Distributed and real-time monitoring of large-scale multi-modal transport systems is
emerging as a solution to meet such demands, thus providing transport actors with novel
decision-making tools for more effectively managing mobility, by reducing costs and
guaranteeing higher-quality services.
The LICIT laboratory of University of Lyon, ENTPE and IFSTTAR is working with academic
(INSA-Lyon, INRIA, LIRIS, CNR-Italy, University of Illinois Chicago, University of Sannio,
University of Tartu) and industrial partners (Orange, Lyon Metropole) in the framework of
the French ANR project “PROMENADE” (Platform for Resilient Multi-modal Mobility via
Multi-layer Networks & Real-time Big-Data Processing).
The PROMENADE project aims to improve transport resilience via big real-time data
monitoring, complex networks and machine learning solutions by disposing of large-scale
and multi-source datasets on human mobility.
An open source customisable and extensible platform will be proposed to address the
different challenges related to engineering the resilience of large-scale urban transport
networks, by integrating tools, algorithms and components to perform heterogeneous data
collection (IoT sensors, server APIs, mobile networks, social networks, etc.), big data
processing and multi-source data mining.
In this context, we are looking for an enthusiastic postdoc with strong background in
Distributed Systems Design, IoT and Big Data Processing as well as in Software
Engineering, and with an interest and skills in Machine Learning and Data Analysis.
*** DESCRIPTION OF THE POSTDOC ACTIVITIES ***
• The goal of this postdoc is to contribute to the design and prototyping of the
architecture of the PROMENADE platform, as well as to the development of the algorithms
and solutions for data-driven mobility and network analysis.
• The postdoc will also be involved in supporting the activities of three PhD students,
whose thesis topics are related respectively to:
• developing a data-driven modelling framework for (real-time) reconstruction and
analysis of mobility practices from multi-source data;
• complex network engineering of large-scale approaches for real-time computation of
• definition of new resilience indicators for multi-modal transport networks via
simulation-driven stress testing and dynamic control strategies for new generation
resilient transport networks and systems.
• The postdoc could be involved in teaching activities related to Intelligent
Transportation Systems, Data mining and machine learning.
The platform will be implemented by using multiple technologies suited for big data
processing, machine learning and IoT integration (Spark, Scala, Python, Kubernetes,
*** REQUIREMENTS ***
• The candidate shall hold a PhD in Computer Science, on a topic related to big data,
distributed computing, scalable software engineering, machine learning for big data.
• Proven experience on IoT, Big Data Architectures and Technologies (Big Data processing
and management) and distributed systems: Hadoop, Spark, Scala, Kubernetes, HBase;
• Good scripting and coding skills (bash, java, scala, pyspark, python);
• Autonomous and team working capabilities.
*** OFFER DETAILS ***
• The postdoc will have the opportunity to work in a stimulating research environment
including both academic and industrial collaborations, to participate to both computer
science and transportation conferences, as well as spending short abroad research periods
in the partner institutions.
• Duration: 12 months (with possibility of an extension to 18 months)
• Net Salary: 2,000 – 2,300 € per month depending on the experience.
• Start Date: end of 2019/beginning of 2020.
• Hosting team: LICIT laboratory (http://licit.ifsttar.fr
), Lyon, France (EU).
*** APPLICATION INSTRUCTIONS ***
Applications in PDF format or informal enquiries by email to angelo.furno(a)ifsttar.fr are
Applications, written in English should include:
• Curriculum Vitae (including your contact address, work experience, publications,
• Cover letter
Deadline for applications: 30 October 2019.
Screening of applications starts immediately and will continue until the position is
filled. Therefore, early applications are encouraged.
The starting date is negotiable, but preferably it is fixed on November 1st, 2019.
*** REFERENCES ***
Some recent papers of the team in line with the scope of the PROMENADE project and the
required post-doc profile:
• Katsikouli P., Fiore M., Furno A., Stanica R. (2019, June). Characterizing and Removing
Oscillations in Mobile Phone Location Data. In 2019 IEEE 20th International Symposium on
"A World of Wireless, Mobile and Multimedia Networks" (WoWMoM).
• Castiello A., Fucci G., Furno A., Zimeo E. (2018, December). Scalability Analysis of
Cluster-based Betweenness Computation in Large Weighted Graphs. In 2018 IEEE International
Conference on Big Data (Big Data).
• Furno A., El Faouzi N.E., Sharma R., Zimeo E. (2018, December). Fast Approximated
Betweenness Centrality of Directed and Weighted Graphs. In 2018 International Conference
on Complex Networks and their Applications. Springer International Publishing.
• Henry E., Bonnetain L., Furno A., El Faouzi N.E., Zimeo E. (2019, June).
Spatio-temporal Correlations of Betweenness Centrality and Traffic Metrics. In 6th
International Conference on Models and Technologies for Intelligent Transportation Systems
• Fekih M., Bellemans T., Smoreda Z., Bonnel P., Furno A., Galland S. (2019, January).
Suitability of Cellular Network Signaling Data for Origin-Destination Matrix Construction:
A Case Study of Lyon Region (France). In 98th Transportation Research Board Annual Meeting
• Bonnetain L., Furno A., Krug J., El Faouzi N.E. (2019, January). Can we map-match
individual cellular network signaling trajectories in urban environments? A data-driven
study. In Transportation Research Record (TRR).
• Gauthier P., Furno A., El Faouzi N.E. (2018, August). Road network resilience: how to
identify critical links in presence of day-to-day disruptions?. In Transportation Research
Thanks and best regards,
Researcher, Université de Lyon (France)
LICIT laboratory (IFSTTAR-ENTPE)
IXXI - Lyon’s Institute of Complex Systems
phone: (+33) 04 78 65 68 70