Job offer

European Commission; France; Sur place; EUR 40 000 - 60 000; Plein temps

Résumé du poste

A leading research institution is offering a PhD opportunity focused on developing methods to analyze and correct blending effects in astronomical surveys. The candidate will address significant challenges in observational cosmology as part of an international collaboration, enhancing understanding of dark energy.

Qualifications

• Candidate should have a background in cosmology or physics, preferably with programming skills in machine learning.
• Experience with astronomical data analysis and familiarity with survey methodologies are advantageous.

Responsabilités

• Develop innovative methods to quantify and correct blending effects in astronomical surveys.
• Utilize machine learning for joint analysis of LSST, Euclid, and Roman data.

Connaissances

Machine Learning

Probabilistic Deep Neural Networks

Data Analysis

Formation

PhD in Cosmology or related field

Outils

LSST Data

Euclid Data

Roman Data

Description du poste

Organisation/Company CNRS Department Laboratoire de physique subatomique et de cosmologie Research Field Physics Researcher Profile First Stage Researcher (R1) Country France Application Deadline 9 Jul 2025 - 23:59 (UTC) Type of Contract Temporary Job Status Full-time Hours Per Week 35 Offer Starting Date 1 Oct 2025 Is the job funded through the EU Research Framework Programme? Not funded by a EU programme Is the Job related to staff position within a Research Infrastructure? No

Offer Description

The Grenoble Laboratory of Subatomic Physics and Cosmology (LPSC)
(http://lpsc.in2p3.fr ) is a joint research unit associating CNRS-IN2P3, Université Grenoble Alpes (UGA) and Grenoble INP school, for an average staff of around 230.
The PhD student will be assigned́ to the “Observational Cosmology” group made up of 10 LPSC staff and will be placed under the direct hierarchical authority of the group/department manager.
His/her thesis supervisor will be Cyrille DOUX.

Understanding the origin of the Universe's accelerated expansion and the nature of dark energy is one of the major challenges in modern cosmology. To tackle this, large-scale astronomical surveys such as LSST (Vera C. Rubin Observatory), Euclid (ESA), and Roman (NASA) will provide unprecedented and complementary data on billions of galaxies, ushering in a new era of observational cosmology.

Among the key tools leveraged by these surveys is weak gravitational lensing, which enables mapping the matter distribution in the Universe. However, this method is particularly sensitive to blending, the apparent overlap of astronomical sources (galaxies, stars, etc.), which distorts shape, brightness, and redshift measurements—thereby introducing significant systematic biases in cosmological analyses.

This PhD project aims to develop innovative methods to quantify, correct, and mitigate the effects of blending, by leveraging the unique complementarity of the three surveys. In particular, the high-resolution space-based images from Euclid and Roman can be used to train or inform models applied to LSST data, which are deeper but affected by atmospheric turbulence.

The research will unfold in three main phases:

Characterizing blending in early LSST data (commissioning images), using advanced probabilistic methods developed within the host team.

Assessing the impact of blending on key cosmological observables (gravitational shear, galaxy clustering), based on realistic simulations of upcoming surveys.

Developing correction algorithms using machine learning, notably probabilistic deep neural networks capable of integrating multi-band, multi-instrument data. These tools will then be used for a joint analysis of LSST, Euclid, and Roman data, aiming to maximize cosmological information while controlling systematics.

This thesis is embedded in a vibrant international research context. It will contribute to improving the reliability of cosmological measurements from next-generation surveys, with the goal of providing tighter constraints on dark energy properties. The PhD candidate will join the observational cosmology group at LPSC in Grenoble, collaborate closely with the University of Chicago, and be actively involved in the LSST DESC and Euclid international collaborations.

PhD funded by a CNRS grant in collaboration with a team at the University of Chicago with regular meetings