Postdoctoral Appointee, Federated Learning and Foundation Models

Job overview

The Postdoctoral Appointee, Federated Learning and Foundation Models role is onsite in Lemont, IL. The expected hiring range is $72,879–$121,465 per year. The appointee joins Argonne National Laboratory's Mathematics and Computer Science Division, collaborating with universities and other national laboratories. The researcher studies optimization and training dynamics for large-scale machine learning and develops efficient, reliable, and privacy-aware algorithms for distributed and federated settings. The work combines theory, experiments, and open-source software, with publication and collaboration responsibilities that advance scientific machine learning research.

What you'll do

• Research optimization and training dynamics for large-scale machine learning
• design, implement, and benchmark distributed and federated training algorithms
• contribute reproducible research software to open-source projects
• publish and present results
• collaborate across institutions
• mentor students
• develop research independence and professional skills.

What we're looking for

Skills & competencies

postdoctoral appointee

federated learning

foundation models

machine learning

optimization

distributed training

privacy

python

pytorch

open-source software

lemont il

argonne national laboratory

Work arrangement

• Weekend coverage required

Benefits & perks

• Comprehensive benefits are part of the total rewards package
• details are available through Argonne's employee benefits information.

Why this role

Requires a Ph.D. completed within the last 0–5 years and a publication record in relevant machine learning research, plus Python, PyTorch, and GPU model-training skills. Postdoctoral appointees are expected to lead papers.

About the employer

Argonne National Laboratory is hiring for this role. Industry: Space Research and Technology. Sector: 92.