Gaia Variable Star ML Research Assistant

UNC-Chapel Hill; North Carolina; On-site; USD 42,000 - 54,000; Full time

Job summary

UNC-Chapel Hill seeks a Research Assistant to support the Gaia Variable Star Machine Learning project. The role focuses on applying machine learning and functional data analysis to map variability in stars using Gaia DR3 multi-epoch photometry and HR diagram data.

You will contribute as the primary day-to-day researcher, coordinating with the PIs and collaborating with the CAS Seed Program team through discovery, characterization, and dissemination phases.

Qualifications

• Research experience with Gaia DR3 multi-epoch photometry or HR diagram/color-magnitude diagram analysis
• Experience with functional data analysis, smoothing splines, or unsupervised clustering methods
• Experience using high-performance computing (e.g., UNC Longleaf) or GPU cloud computing resources
• Familiarity with time-domain survey data (TESS, ZTF, ASAS-SN) or variable star astrophysics
• Experience building interactive web-based data visualization tools

Responsibilities

• Support the CAS Seed Program project: Gaia in Technicolor: mapping variability of stars through ML.
• Serve as primary day-to-day researcher during discovery, characterization, and dissemination phases.
• Work under supervision of project PIs and meet bi-weekly with them.

Skills

Gaia DR3 photometry

HR diagram analysis

Functional data analysis

Smoothing splines

Unsupervised clustering

High-performance computing

GPU cloud computing

Time-domain data

Variable star physics

Interactive visualization

Tools

UNC Longleaf

Job description

UNC-Chapel Hill seeks a Research Assistant to support the Gaia Variable Star Machine Learning project. The role focuses on applying machine learning and functional data analysis to map variability in stars using Gaia DR3 multi-epoch photometry and HR diagram data.

You will contribute as the primary day-to-day researcher, coordinating with the PIs and collaborating with the CAS Seed Program team through discovery, characterization, and dissemination phases.