Offre en lien avec l’Action/le Réseau : – — –/– — –
Laboratoire/Entreprise : LIFAT (EA 6300), Université de Tours
Durée : 18 months
Contact : nicolas.ragot@univ-tours.fr
Date limite de publication : 2023-09-30
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 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.
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.
The project actors are: BRGM, Université d’Orléans, Université de Tours, CNRS, INRAE, and ATOS and ANTEA companies.
Sujet :
While the BRGM 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 build new prediction models able to integrate several sources of information like:
– meteorological data
– spatial information, i.e. geolocalization of sensors and locations of predictions to be made; topological information such as altitude
– integration of knowledge from mechanistic models as well as from expert knowledge (impact of attributes and variables used)
– etc.
The scientific locks are clearly related to:
– multivariate time series
– short-term to long term predictions (horizon)
– going from local predictors to ‘connected predictors’, i.e. how to use information coming from sensors spread over the area of study
And if possible:
– considering heterogenous data (time series, climatic data, topological information, combination with other models…)
– having an idea of how continuous learning (work of a PhD) could be done on such models.
Studying transformers and Spatio-Temporal Graph Neural Networks will be particularly investigated.
Of course, models will have to be implemented, learnt and compared with classical models on benchmarks.
Profil du candidat :
The candidate should be experimented with machine learning (Python) and time series.
Formation et compétences requises :
PhD in machine learning (computer sciences or applied mathematics)
Skills:
– a strong experience in data analysis and machine learning (theory and practice of deep learning in python) is required
– experiences/knowledge in time series prediction and environmental science is welcome
– 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 :
Affiliation: Computer Science Lab of Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)
Document attaché : 202307171132_Fiche de poste Pdoc Junon.pdf