M/F Young researcher in Astrophysics

Job Description

Missions

The successful candidate will:
• Lead the development of advanced deep learning methods to detect gravitationally lensed supernovae in Rubin/LSST data.
• Work closely with the laboratory's data center and machine learning division to optimize algorithms and data-processing pipelines.
• Contribute to the observational follow-up of confirmed lensed supernovae (imaging and/or spectroscopy).
• Perform comparative analyses using synthetic spectra and early-time light curves.
• Build and maintain a catalog of static gravitational lenses and a census of confirmed and candidate lensed supernovae.
This catalog will constitute a major deliverable of the project and a valuable resource for the broader scientific community.

Activity

The main task will be to lead the development of advanced deep-learning methods to discover the rare gravitationally lensed supernovae in Rubin/LSST data.This work will be done with the support from the machine learning division of the LAM data centre. The candidate will also contribute to the observational follow-up of confirmed lensed SNe, and to the comparison with synthetic spectra and light curves of SNe at early phases.

Your Profil

Skills

- Programming
- Machine & deep learning techniques
- Neural networks
- Observations

Your Work Environment

The work will be done in the context of the recently-awarded international French-German grant "SuperEarly: Constraining Supernova Progenitors through Strong Lensing in the Rubin LSST Era". This three-year project will allow us to obtain the earliest rest-frame UV observations of SNe ever made. Our project builds upon the results from the HOLISMOKES program that was funded by the European Research Council. The PI of that program, Sherry Suyu (TUM/MPA, Germany), is part of the team of Supearly. At LAM, in addition to the project co-PI J.-C. Bouret, the postdoc will also work closely with postdoc Raoul Canameras (expert on deep-learning algorithms to search for lensed supernovae) and a PhD student to be recruited for this project. There will be regular collaboration visits (twice per year) to our German collaborators on the Garching campus near Munich, Germany. There would also be a possibility for more extended visits (up to 1 year, TBD) at the European Southern Observatory (ESO) on the same campus, where the co-PI S. Blondin is also located. The postdoc will benefit from full data access rights to the Rubin/LSST project.
Note that although the initial appointment is for two years, a third year is possible (and already funded).

Compensation and benefits

Compensation

€3,131.32 to €4,806.76 gross/month, depending on experience.

Annual leave and RTT

44 jours

Remote Working practice and compensation

Pratique et indemnisation du TT

Transport

Prise en charge à 75% du coût et forfait mobilité durable jusqu’à 300€

About the offer

About the CNRS

The CNRS is a major player in fundamental research on a global scale. The CNRS is the only French organization active in all scientific fields. Its unique position as a multi-specialist allows it to bring together different disciplines to address the most important challenges of the contemporary world, in connection with the actors of change.

CNRS

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