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Laboratoire d’Annecy de Physique Théorique, CNRS

Two Postdoctoral Positions in Simulation-Based Inference for Large-Scale Structure and High-Energy Astrophysics Laboratoire d’Annecy de Physique Théorique in France

Degree Level

Postdoc

Field of study

Computer Science

Funding

Two postdoctoral appointments funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair. Position 1 is a two-year appointment and Position 2 is a three-year appointment. Salary is on the USMB scale, commensurate with experience, and includes social security and health coverage. The positions also provide access to CNRS/LAPTh computing resources and generous travel support.

Deadline

Nov 20, 2026

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Country

France

University

Laboratoire d’Annecy de Physique Théorique, CNRS

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Keywords

Computer Science
Mathematics
Astronomy
Astrophysics
Cosmology
Gamma-ray Astronomy
Dark Matter
Large-scale Structure
Statistics
Physics
ML

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About this position

Two postdoctoral positions are open at the Laboratoire d’Annecy de Physique Théorique (LAPTh) in Annecy, France, within the ADACSI project (Accelerating Discoveries in Astrophysics and Cosmology with Simulation-based Inference). The project is funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair.

The research combines astrophysics, cosmology, large-scale structure, gamma-ray astrophysics, simulation-based inference, machine learning, and statistics. The goal is to build a multi-probe framework that jointly analyzes large-scale structure data and gamma-ray observations to constrain astrophysical source populations and dark-matter signals. The framework will be validated on synthetic data and then applied to public datasets such as Fermi-LAT and DESI.

Position 1 focuses on LSS forward modelling and SBI methodology, including fast simulations of spectroscopic galaxy surveys and CMB lensing, building on and extending the SimBIG pipeline. Position 2 focuses on gamma-ray astrophysics and data analysis, including modelling gamma-ray emitters in the cosmic web and developing SBI pipelines for Fermi-LAT data, first standalone and then jointly with LSS.

Applicants should have a PhD in astrophysics, cosmology, or a related field by the start date, strong programming skills, and experience with machine learning applied to astrophysics/cosmology. For Position 1, experience with LSS simulations, galaxy redshift surveys, and HPC is preferred. For Position 2, experience with gamma-ray data analysis and astrophysical interpretation is preferred. Candidates with strong machine-learning or statistics backgrounds moving into astrophysics are also welcome.

The appointments are co-supervised by Dr. Azadeh Moradinezhad and Dr. Francesca Calore in the Astroparticle and Cosmology group at LAPTh, with external collaborators from the University of Amsterdam, the Flatiron Institute, and the SimBIG collaboration. The positions include access to CNRS/LAPTh computing resources, generous travel support, and social security and health coverage. Position 1 is for two years; Position 2 is for three years. The expected start date is 1 October 2027.

Apply through AcademicJobsOnline by submitting a cover letter (specifying Position 1, Position 2, or both), a CV with publications, a research statement of up to four pages, and arranging three letters of recommendation. Applications received before 2026-11-20 will receive full consideration; the positions remain open until filled.

Funding details

Two postdoctoral appointments funded by the Multidisciplinary Institute for Artificial Intelligence (MIAI) through an AIforScience Research Chair. Position 1 is a two-year appointment and Position 2 is a three-year appointment. Salary is on the USMB scale, commensurate with experience, and includes social security and health coverage. The positions also provide access to CNRS/LAPTh computing resources and generous travel support.

What's required

A PhD in astrophysics, cosmology, or a related field completed by the start date; strong programming skills; working knowledge of machine learning applied to astrophysics and cosmology, especially simulation-based inference. For Position 1, experience with large-scale structure simulations and analysis, ideally galaxy redshift surveys and HPC. For Position 2, experience with gamma-ray data analysis and astrophysical interpretation. Applicants with a strong machine-learning or statistics background moving into astrophysics are welcome.

How to apply

Apply via AcademicJobsOnline. Submit a cover letter indicating Position 1, Position 2, or both; a CV with publications; a research statement up to four pages; and arrange three letters of recommendation. Applications received before 2026-11-20 receive full consideration.

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