Deep Learning on Multimodal Data for the Supervision of Sensitive Sites

When:
10/03/2018 – 11/03/2018 all-day
2018-03-10T01:00:00+01:00
2018-03-11T01:00:00+01:00

Annonce en lien avec l’Action/le Réseau : aucun

Laboratoire/Entreprise : IRISA / Atermes
Durée : 36
Contact : elisa.fromont@irisa.fr;sebastien.lefevre@irisa.fr
Date limite de publication : 2018-03-10

Contexte :
ATERMES is an international mid-sized company, based in Montigny-le-Bretonneux (near Paris) with a strong expertise in high technology and system integration from the upstream design to the long-life maintenance cycle. It has recently developed a new product, called BARIERTM (“Beacon Autonomous Reconnaissance Identification and Evaluation Response”) which provides operational and tactical solutions for mastering borders and areas. Once in place, the system allows for a continuous night and day surveillance mission with a small crew in the most unexpected rugged terrain. BARIER™ is expected to find ready application for temporary strategic site protection or ill-defined border regions in mountainous or remote terrain where fixed surveillance modes are impracticable or overly expensive to deploy.

Sujet :
The project aims at providing a deep learning architecture and algorithms able to detect anomalies (mainly persons) from multimodal data. The data are considered “multimodal” because information about the same phenomenon can be acquired from different types of detectors, at different conditions, in multiple experiments, etc. Among possible sources of data available, ATERMES provides Doppler Radar, active-pixel sensor data (CMOS), different kind of infra-red data, the border context etc.
The PhD candidate will need to survey the recent literature about multi-source and multimodal learning with deep neural networks (e.g. [1],[2],[3],[4],[5],[6]) as well as the literature on domain adaptation with neural networks (e.g. [3],[7],[8]) since the data will be acquired from different outdoor contexts. He is expected to propose new solutions that could be integrated in the BARIER™ system developed by ATERMES.

Profil du candidat :
We look for highly motivated candidate with the following skills/diploma:
– A master’s degree in computer science;
– Some proven skills in machine learning in general and deep learning in particular;
– Some background in computer vision;
– Some proven skills in programming, preferably in Python and Tensorflow;
– A very good level (written and oral) in English and a good ability to communicate with others;
– A good autonomy

Formation et compétences requises :
See “profil du candidat”

Adresse d’emploi :
The PhD candidate will work part time at IRISA (Rennes and/or Vannes) and part time in the ATERMES company in Paris (the exact percentage of time spent in all the facilities will be discussed).

Document attaché : PhD-CIFRE-Atermes.pdf