Postdoctoral Research Associate I - Prize Fellow in Cosmological Data Analysis

Fellows are expected to conduct independent research in cosmological data analysis with ongoing and future surveys, in particular SPHEREx, Simons Observatory, Rubin Observatory's LSST, Roman Space Telescope, and DESI. Applicants should outline their research plans to optimally extract cosmological information using one or multiple of these datasets. Priority will be given to candidates who can connect their science to SPHEREx, Simons Observatory, and LSST. Expertise in modeling cosmological observables through analytical methods or numerical simulations, machine learning, statistical inference, and data reduction is beneficial.

As members of the Arizona Cosmology Lab, fellows will be provided with priority access to UA and national supercomputing resources, close mentorship by experienced faculty, and opportunities to co-advise graduate students and mentor undergraduates. UA offers a world-class research environment in astrophysics with strong interdisciplinary programs across the Departments of Astronomy, Physics, Planetary Sciences, the UA Data Science Institute, UA Space Institute, and NSF's NOIRLab.

Start dates range from August to October 2026.

Outstanding UA benefits include health, dental, vision, and life insurance; paid vacation, sick leave, and holidays; UA/ASU/NAU tuition reduction for the employee and qualified family members; access to UA recreation and cultural activities; and more!

The University of Arizona has been recognized for our innovative work-life programs.

• Independent Research in cosmological data analysis with ongoing and future surveys (SPHEREx, Simons Observatory, Rubin Observatory's LSST, the Roman Space Telescope, and DESI).

• Must have completed all the requirements for a PhD. in Astronomy, Physics, Astrophysics, or related science at the time of hire.

• Experience in cosmological data analysis with SPHEREx, LSST, SO, Roman, DESI, or similar surveys.
• Experience in statistical methods or machine learning.
• Experience in numerical simulations and complex analytical models.