Offre en lien avec l’Action/le Réseau : – — –/– — –

Laboratoire/Entreprise : BRGM
Durée : 18 months
Contact : nathalie-f.abadie@ign.fr
Date limite de publication : 2021-03-19

Contexte :
SURICATE-Nat (www.suricatenat.fr) is a collaborative platform for the semi-automatic analysis of tweets written in French related to natural disasters (for instance, earthquakes or floods). This platform aims to exploit the testimonies immediately after a natural disaster in order to promote a rapid rise of information by «citizen sensors». This involves extracting the main information from the tweets: type of disaster, damage, location etc. Location is a particularly important piece of information as it helps the relief agencies to deal with the disaster effectively, but state of the art approaches for tweet geolocalization often fail to provide accurate location.

Sujet :
The main mission of the post-doctorate fellow would therefore be to propose new approaches to improve the accuracy and the precision of the automatic geolocalization of tweets, which is a necessary step to accurately describe the effects of natural disasters.

He/she will contribute to the scientific state of the art in several fields:
– recognition, extraction and contextualization of geographic information that can be found in tweets messages,
– spatial named entities resolution and text geocoding.

The successful candidate will work in direct collaboration with researchers from both BRGM and IGN research teams, as well as researchers from the ANR funded project « RéSoCIO», having an established expertise in geographic information extraction, spatial named entity resolution, knowledge management and machine learning.

Profil du candidat :
PhD in computer science, computational social science, computational linguistics or geographic information science with knowledge in Natural Language Processing:
With less than five years of postdoctoral experience
You need to have academic publications in related works

Formation et compétences requises :
– Text mining/NLP methods (particularly Deep Learning models such as BERT and its French implementations, such as FlauBERT, camemBERT, etc.) and libraries (NLTK, Spacy, etc.)
– Very good programming skills (Python))
– Knowledge on GIS (QGIS, PostGIS and spatial SQL) and statistical analysis tools (R)
– Ability to understand questions regarding research and transfer
– Spoken and Written French and English
– Knowledge of natural hazards issues would be appreciated

Adresse d’emploi :
BRGM, Orléans, France

Document attaché : 202102261617_Postdoctoral position BRGM-IGN_Geolocating Tweets.pdf