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Master internship and PhD studentship opportunity (2024)
High-performance information extraction from cosmic web probes
AnnouncementWelcome!
My name is Florent Leclercq. I am a research scientist (Chargé de recherche CNRS) at the Institut d'Astrophysique de Paris (IAP). I hold an interdisciplinary position at the interface between astrophysics (Institut national des sciences de l'Univers, INSU) and information science (Institut des sciences de l'information et de leurs interactions, INS2I). I work in the fields of numerical cosmology and artificial intelligence, focusing in particular on the analysis of galaxy survey data. I have been a member of the Aquila Consortium since it was created, in 2016. I am also a member of the Euclid Consortium, where I currently co-lead the "Additional Probes" work package of the Galaxy Clustering Science Working Group.
My current research interests are related to the study of the cosmological large-scale structure using statistical inference, machine learning, and high-performance computing tools. I am particularly interested constraining cosmology from the large-scale structure, in the initial conditions from which it originates, its formation history and the description of the cosmic web.
Please check the full version of my CV, my list of publications and my list of communications.
News
- 11-10-2023
New master internship and PhD studentship opportunity: High-performance information extraction from cosmic web probes in the INFOCW project. See Supervision and mentoring. - 03-02-2023
Pages updated: pySELFI (v2.0) and Lotka-Volterra simulator. - 09-01-2023
Submitted paper: Higher-order statistics of the large-scale structure from photometric redshifts. - 23-09-2022
New paper: Simulation-based inference of Bayesian hierarchical models while checking for model misspecification (MaxEnt'22 proceedings).
Upcoming Talks
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Dealing with systematic effects: the issue of robustness to model misspecification
28-11-2023, Debating the potential of machine learning in astronomical surveys #2 conference, Institut d'Astrophysique de Paris, Paris, France

Evolution of cosmological simulations over the last 50 years
I recently scanned the literature for the purpose of following and plotting the number of particles used in \(N\)-body simulations over the last five decades.
08-04-2020
Algorithms for likelihood-free cosmological data analysis
Likelihood-free inference provides a framework for performing Bayesian inference in cosmology, by replacing likelihood calculations with data model evaluations.
25-04-2019Public data and software

pySELFI
pySELFI is a publicly-available implementation of the Simulator Expansion for Likelihood-Free Inference algorithm, allowing primordial power spectrum inference from black-box galaxy surveys.