About this position
- The Laboratoire d’Annecy de Physique Théorique (LAPTh) invites
- applications for two postdoctoral positions within ADACSI (Accelerating
- Discoveries in Astrophysics and Cosmology with Simulation-based
- Inference), funded by the Multidisciplinary Institute for Artificial
- Intelligence (MIAI) through an AIforScience Research Chair.
- ADACSI will develop a multi-probe simulation-based framework for the
- joint analysis of large-scale structure (LSS) and gamma-ray data. By
- combining these complementary probes of the same underlying matter
- distribution, the project aims to sharpen constraints on astrophysical
- source populations and dark-matter signals, exploiting statistical
- correlations across datasets to break degeneracies that limit
- single-probe analyses. The framework will be validated on synthetic
- data and applied to public datasets, e.g. Fermi-LAT and DESI.
The two postdocs will be recruited simultaneously and will work as a
team toward a single joint pipeline.
- Position 1 — LSS forward modelling and SBI methodology. Developing
- fast simulations of the LSS observables, in particular data from
- spectroscopic galaxy surveys and CMB lensing and the inference
- machinery, building on and extending the SimBIG pipeline. This forward
- model is also the baseline for the gamma-ray side of the project.
- Position 2 — Gamma-ray astrophysics and data analysis. Modelling how
- gamma-ray emitters populate the cosmic web and linking source
- populations to the underlying halo and galaxy distribution; developing
- SBI pipelines for Fermi-LAT data, first standalone and then jointly
- with LSS.
- Qualifications. 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,
- in particular simulation-based inference. For Position 1, experience
- with LSS simulations and analysis, and ideally galaxy redshift surveys
- and HPC. For Position 2, experience with gamma-ray data analysis and
- its astrophysical interpretation. Applicants with a strong
- machine-learning or statistics background moving into astrophysics are
- welcome.
- Supervision and environment. Both positions will be co-supervised by
- Dr. Azadeh Moradinezhad and Dr. Francesca Calore, within the
- Astroparticle and Cosmology group at LAPTh (astrocosmolapth.com).
- External collaborators include Christoph Weniger (GRAPPA, University
- of Amsterdam) and Francisco Villaescusa-Navarro (Flatiron Institute,
- New York), as well as SimBIG collaboration. A PhD student and several
- Master’s students will also be recruited within ADACSI, offering
- co-supervision opportunities. The positions provide access to
- CNRS/LAPTh computing resources and generous travel support and, as an
- MIAI-funded chair, connect the postdocs to the institute’s
- interdisciplinary AI community; the group also benefits from its
- proximity to the University of Geneva and CERN.
- Terms. Position 1 is a two-year appointment; Position 2 is a
- three-year appointment. Salary on the USMB scale, commensurate with
- experience, including social security and health coverage. The
- expected start date is 1st October 2027with some flexibility.
Applications. Submit via AcademicJobsOnline:
- a cover letter, indicating Position 1, Position 2, or both;
- a CV with a list of publications;
- a research statement (up to four pages);
- three letters of recommendation, arranged by the applicant.
- Applications received before November 20th - 2026 will receive full
- consideration; the positions remain open until filled. Shortlisted
- candidates will be invited to a remote interview.
Contact. Dr. Francesca Calore (francesca.calore@lapth.cnrs.fr) and Dr.
Azadeh Moradinezhad (azadeh.moradinezhad@lapth.cnrs.fr)
How to apply
- Read the full advert on the source site — it carries the authoritative terms.
- Email the contact below with your CV and a short, specific message. See Emailing Professors.
- Prepare your SOP, CV, transcripts and referees before the deadline.
- Apply through the university's own portal. Never pay a fee to a third party.