Postdoctoral Appointee - Machine Learning in Cosmology

Postdoctoral position offered in the Computational Science (CPS) Division, which will be jointly held with the Cosmological Physics and Advanced Computing Group (CPAC) Group in Argonne’s High Energy Physics Division. Candidates should have research interests in large-scale structure probes of cosmology and the dynamics of cosmological structure formation, along with a strong focus on the application of machine learning, especially deep learning, in novel applications. Candidates that have extensively used machine learning in scientific fields such as astrophysics, fluid dynamics, and statistical mechanics will also be considered. Cosmological research within CPS and CPAC covers theory, modeling, and observations targeted at dark energy, dark matter, primordial fluctuations, inflation, and neutrinos. Theory, modeling, and analysis efforts emphasize multi-wavelength survey science, cluster cosmology, and large-scale cosmological simulations.

The observational effort focuses on participation in optical sky surveys (DES, DESI, Rubin Obs/LSST, SPHEREx), CMB observations (SPT), and cross-correlations between the two. The work will involve strong collaborations with applied mathematicians, computer scientists, and statisticians; access to world-leading supercomputing resources will be available.

Applicants should send 1) a CV and 2) a brief statement of research interests to the URL below for follow-up action. A Ph.D. is required but must not have been obtained prior to 2018.