Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : LIFAT, Université de Tours
Durée : 6 months (end in Jun
Contact : nicolas.ragot@univ-tours.fr
Date limite de publication : 2026-01-16
Contexte :
The JUNON project, driven by the BRGM, is granted from the Centre-Val de Loire region through ARD program (« Ambition Recherche Développement ») which goal is to develop a research & innovation pole around environmental resources (agriculture, forest, waters…). The main goal of JUNON is to elaborate digital services through large scale digital twins in order to improve the monitoring, understanding and prediction of environmental resources evolution and phenomena, for a better management of natural resources. Digital twins will allow to virtually reproduce natural processes and phenomena using combination of AI and environmental tools.
JUNON will focus on the elaboration of digital twins concerning quality and quantity of ground waters, as well as emissions of greenhouse gases and pollutants with health effects, at the scale of geographical area corresponding to the North part of the Centre-Val-de-Loire region.
These digital twins will rely on geological and meteorological knowledge and data (time series), as well as physic-based models.
The project actors are: BRGM, Université d’Orléans, Université de Tours, CNRS, INRAE, and ATOS and ANTEA companies. There are 5 work packages (WP):
1. User’s needs and geological knowledge for ground water
2. User’s needs and biological/chemical knowledge about pollutants and greenhouse gases
3. Data management and data mining
4. Time series prediction
5. Aggregation and realization of digital twins themselves
The postdoctoral position will be in the WP 4, focused on the prediction of quantity of ground waters. There will be strong interactions with WP 1 and 3 (BRGM) through postdocs and engineers. The work will be supervised by the LIFAT – RFAI and you will have to interact with one PhD student in JUNON as well. Interaction with the RFAI group and other PhDs working on similar subjects will also be done.
Sujet :
While the BRGM (a postdoc to be recruited) will have in charge to collect and arrange data (ground waters levels at different locations) and to benchmark predictions with mechanistic models as well as with classical prediction AI tools, the goal of the postdoc will be
– To set up an evaluation protocol and SOTA approaches to design a specific competition for an upcoming conference. The protocol will relies on previous analysis and data and will focus on infrastructure and meachnisms to deliver data to participants according to different scenarii including continual learning ones
– to build new prediction models (able to integrate several sources of information ; using correlation between mulitple sensors ; using knowledge transfer or domain adaptation, etc.)
Profil du candidat :
The position is initially for a postdoc position but candidates with a Master of Science degree and strong skills and experience in Machine Learning for Time Series could also apply.
Formation et compétences requises :
– required:
– strong experience in data analysis and machine learning (theory and practice of deep learning in python)
– experiences/knowledge in time series prediction with SOTA deep learning approaches
– interest or experiences with environmental science (hydrogeology, air pollution…)
– curiosity and ability to communicate (in English at least) and work in collaboration with scientists from other fields
– ability to propose and validate new solutions and to publish the results
– autonomy and good organization skills
Adresse d’emploi :
Computer Science Lab of Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)
https://www.rfai.lifat.univ-tours.fr
64 av. Jean Portalis,
37200 TOURS
FRANCE
Document attaché : 202510311520_Fiche de poste Pdoc 2 Junon.pdf