Reference : UMR5126-DELMAR-002
Nombre de Postes : 1
Workplace : TOULOUSE
Date of publication : Thursday, January 12, 2023
Type of Contract : FTC Technical / Administrative
Contract Period : 17 months
Expected date of employment : 1 April 2023
Proportion of work : Full time
Remuneration : 2213.87€ and 2335.41€ gross per month depending on experience
Desired level of education : 5-year university degree
Experience required : 1 to 4 years
Within the CESBIO laboratory, the incumbent will be in charge of a research study on the use of machine learning methods to improve the consideration of aerosol types in the MAJA atmospheric correction chain. The aim is to explore single and multi-variable regression approaches in order to select the most efficient one, which is the result of a compromise between simplicity and speed, accuracy and robustness. The long-term objective is to implement this method in the MAJA chain, which is notably used at CNES in the operational processing of Sentinel-2 data distributed to the community, but also to prepare the use of the chain for the future TRISHNA space mission.
The work required will consist in :
- getting to grips with the radiative transfer calculation tools and learning libraries
- developing methods and tools for generating the training set in an HPC environment
- coding and evaluate the performance of the different learning methods;
- contributing to the implementation and validation of the prototype of the selected algorithm in the MAJA chain.
The incumbent should have a strong background in computer science and mathematics, with some knowledge of data science if possible. A good knowledge of Python is essential, and a good knowledge of learning method libraries (Pytorch, scikit-learn, xarray, Pandas) would be appreciated.
Good knowledge of English
The Centre d'Études Spatiales de la Biosphère (CESBIO) is a public research laboratory at the forefront of the field of space remote sensing. The laboratory has nevertheless maintained a human scale with a staff of around 100 people who contribute to the development of themes as varied as ecology, water resource management and agronomy using data from Earth observation satellites.
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