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Postdoctoral position in MLearning and Time series

17 Juillet 2023

Catégorie : Post-doctorant

Machine learning for time series prediction in environmental sciences

Profile: PhD in machine learning (computer sciences or applied mathematics)

Duration: 18 months, starting from September 2023; another 18 months postdoc is planned on similar subject after the first one

Affiliation: Computer Science Lab of Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)

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.


How to candidate:

Send the following documents by e-mail to nicolas.ragot [at] before September 15th: a CV, a motivation letter, a short description of your thesis and experiences in machine/deep learning (including projects you were involved in), references from researchers you worked with.



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.

Full description & information here: