The Machine Learning team at the LITIS laboratory, the computer science laboratory of the University
of Rouen Normandy, is looking for a post-doctoral researcher on a 18-months contract, starting as
soon as possible. The position will be financed by the ANR research project CATCH (french acronym for
"Automatic Understanding of Human Sensors Testimonials"), which involves the R&D center of the
company Saagie1, specialized in B2B DataOps solutions, Atmo Normandie2, one of the approved French
air quality monitoring associations and LITIS.
Deep learning – Natural Langage Processing – Sentiment Analysis / Opinion Mining
The ambition of the CATCH project is to propose artificial intelligence and deep learning tools to take
into account and automatically exploit the multitude of human testimonies related to an industrial
accident and its consequences on the environment and health. By involving the population in the
collection and analysis of data, particularly through social networks, and by providing effective means
for interpreting this data, the proposed solution should contribute to providing answers to the
worrying problem of industrial accidents and their consequences.
The overall objective is first to draw up a precise cartography of the nuisances in order to follow the
propagation and the evolution of the phenomena in time, and then to analyze and characterize the
sentiment of the population and its evolution throughout the crisis. To do so, we intend to exploit
testimonials collected on the ODO platform3 of Atmo Normandie, which combines these testimonies
with geographical information, in conjonction with data extracted from the micro-blogging platform
Twitter. Since these data are primarily texts, state-of-the-art approaches from the Natural Language
Processing (NLP) field are favored, in particular, self-supervised deep learning methods such as
Transformers4 that are known to be the most performant today for a wide range of NLP tasks5.
The objective of this research work is twofold:
1. The automatic generation of a map of nuisances around the site of an industrial incident to
monitor the propagation and the evolution of the phenomena over time.
2. The automatic recognition of the population's perception and its evolution throughout the
Related to these tasks, the post-doctoral researcher will be in charge of proposing solution for:
• extracting and linking twitter data with testimonials from the ODO dataset, which is fully
labelled and associates textual testimonies with geographical data. The interest in establishing
this link is to be able to enrich the data from the ODO platform to refine the mapping of
nuisances in real time. This could be achieved for example, by using pseudo-labelling
techniques6 or Constrative Representation Learning methods which have recently been applied
to text data7.
• detecting in all the testimonials collected from Twitter or from the ODO platform, the presence
(or absence) of several pre-identified emotions (e.g. surprise, fear, anger, sadness, disgust, etc.),
several of which can be expressed at the same time.
This research work will therefore involve being familiar with the state-of-the-art NLP deep learning
methods and in particular with their applications to sentiment analysis and opinion mining tasks. It will
also require experience with the use and exploitation of data from Twitter in a data science context.
The successful applicant will:
• possess or be in the process of obtaining a Ph.D. in computer science or applied mathematics,
with a focus on machine learning or data mining.
• have strong programming skills (Java, Python, etc.) and in-depth understanding of statistics and
• have already worked with deep learning architecture dedicated to texts (RNNs, Transformers,
etc.) and/or images (CNNs, FCNs, GANs).
• have a productive publication record.
Your application should include:
• curriculum vitae
• statement of past research accomplishments, career goal and how this position will help you
achieve your goals
• two representative publications
• contact information for at least two references
Location : LITIS lab., University of Rouen Normandy, Rouen, France
Duration : 18-months, starting as soon as possible
Salary : ~2100€ / month (before income tax), including social security coverage in line with French
Applications : open from 01/09/2021 to 31/12/2021
Application must be sent to :
• Simon BERNARD, University of Rouen Normandy, simon.bernar d @univ-rouen.fr
• Clément CHATELAIN, INSA Rouen Normandy, firstname.lastname@example.org
• Alexandre PAUCHET, INSA Rouen Normandy, email@example.com