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2-years postdoc in machine learning for multispectral data unmixing

13 December 2022


Catégorie : Post-doctorant


Contexte:

Blind and semi-blind unmixing problems are ubiquitous in a very wide range of scientific domains from remote sensing to astrophysics. In these domains, the fast development of high resolution/high sensitivity multispectral sensors mandates the development of dedicated analysis tools to extract relevant and interpretable information. With the fastly increasing size of the multi/hyperspectral to process, one of the key challenges to overcome is the development of fast and yet efficient solvers for multispectral data unmixing. The goal of the project is to explore and develop novel machine learning based approaches based on algorithm unrolling to tackle these problems.

Activity:

The postdoc activity will focus on the development of dedicated algorithm unrolling models to tackle blind/semi-blind multispectral unmixing problems. This work will particularly focus on investigated approaches with low level of supervision, which is fundamental in physical applications. The developed algorithms will be tested and validated on X-ray multispectral images in astrophysics in preparation for the Xrism and Athena space mission.

 

Skills:

PhD level in computer science or signal/image processing. Excellent coding skills in python, with a good knowledge of pytorch.

 

Team:

The candidate will be part of a collaboration between the computer science lab at CEA/IRFU (CEA Paris-Saclay) and the Image team at Telecom ParisTech.

 

General information

Funding: two years.

Gross salary starting from 30kEuros/year depending on the experience of the candidate.

Position available now.

Position open to non-EU citizens.

 

More info: https://www.dropbox.com/s/shyplokthuxbedr/Postdoc_HybridMethods.pdf?dl=0