AI for Astrophysics Research Technician

Python programming for scientific applications.

Experience with AI/ML algorithms.

$27.00 - $30.77

The included pay rate or range represents the University’s good faith estimate of the possible compensation offer for this role at the time of posting.

Yes

The University of Chicago offers a wide range of benefits programs and resources for eligible employees, including health, retirement, and paid time off. Information about the benefit offerings can be found in the

Benefits Guidebook

.

This position supports a research project at the intersection of astrophysics, cosmology, and artificial intelligence / machine learning (AI/ML), focused on strong gravitational lensing analysis using simulation-based inference (SBI) with domain adaptation techniques.

The work centers on developing robust, transferable inference pipelines that bridge the gap between synthetic simulations and real observational data from large-scale astronomical surveys such as DES and LSST.

The research technician will contribute to the design and implementation of deep learning methods aimed at addressing fundamental questions in cosmology, working with large datasets and modern AI/ML inference methods throughout the project.

Develop and benchmark simulation-based inference algorithms (e.g., NPE) for strong gravitational lensing parameter estimation.

Implement domain adaptation techniques to improve model robustness and transferability between simulated and real survey data.

Generate and curate strong lensing simulations using tools such as lenstronomy or similar ray-tracing frameworks.

The Department of Astronomy & Astrophysics is focused on teaching, research, and training in the modern field of astrophysics.