Postdoc: Differentiable Cosmology & Rubin/LSST Inference

CNRS - National Center for Scientific Research; France; On-site; EUR 32,000 - 46,000; Full time

Benefits offered by this job

Health cover

Holiday entitlement

Subsidised meals

Transport discounts

French language courses

Job summary

CNRS - Astroparticule et Cosmologie in Paris invites applications for a two-year postdoctoral position starting December 2026. The project, AstroDeep, aims to develop a unified, probabilistic and differentiable framework for cosmological analysis using forward modelling on LSST data and precursor surveys.

The successful candidate will advance differentiable cosmological models, link parameters to observables, and validate methods within a collaborative, international setting with access to

Qualifications

• PhD, or near completion, in astrophysics, cosmology, physics, statistics or a related field.
• Strong ability to perform cosmological simulations and model large-scale structure evolution.
• Experience with gradient-based inference and Bayesian methods is advantageous.

Responsibilities

• Contribute to the AstroDeep project by developing cosmological inference methods based on forward modelling for LSST analysis.
• Develop and validate differentiable cosmological models for the evolution of large-scale structures and shear observables.
• Create inference workflows to quantify biases, uncertainties and robustness to systematics, including gradients.
• Integrate tools into LSST pipelines, document open-source code and disseminate results via papers and talks.

Skills

Cosmological simulations

Large-scale structure evolution

Weak gravitational shear

Differentiable modelling

Bayesian inference

Gradient-based inference methods

Education

PhD in astrophysics or related field

Tools

JAX

PyTorch

Bayesian sampling

High-performance computing

Job description

CNRS - Astroparticule et Cosmologie in Paris invites applications for a two-year postdoctoral position starting December 2026. The project, AstroDeep, aims to develop a unified, probabilistic and differentiable framework for cosmological analysis using forward modelling on LSST data and precursor surveys.

The successful candidate will advance differentiable cosmological models, link parameters to observables, and validate methods within a collaborative, international setting with access to