[Closed] PhD Position: Continual/life long learning for time series prediction in environmental sciences

Keywords: time series; deep learning; continual learning; digital twins for environment; ground water level prediction

Duration: 3 years, starting from September 15th 2023
Affiliation: Computer Science Lab of Université de Tours (LIFAT), Pattern Recognition and Image Analysis Group (RFAI)

Supervisors: Nicolas RAGOT MCF HDR, Thierry Brouard MCF (LIFAT-Tours) 

Grant: around 1600€-1700€/month

Profile: Master or Engineering degree or equivalent in computer sciences (Machine learning, data sciences) or applied mathematics

– a good 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
– autonomy and good organization skills

How to candidate:

Send the following documents by e-mail to nicolas.ragot [at] univ-tours.fr before 20th of June: a CV, a motivation letter, a short description of your experiences in machine/deep learning, references from academics.