Postdoctoral Appointee - Federated Learning and Foundation Model
Argonne National Laboratory · Posted Nov 3, 2026
- Lemont, Illinois, United States
- On-site
- Full-time
- $73K–$121K per year
- 0–5 years
A research position focused on optimization and training dynamics for large-scale machine learning, specifically within distributed and federated settings and foundation models.
Compensation
The expected hiring range for this position is $72,879.00-$121,465.00.
Responsibilities
- Conducts research on optimization and training dynamics for large-scale machine learning, combining analysis with careful experiments.
- Designs, implements and benchmarks new training algorithms for distributed and federated settings, with attention to efficiency, privacy and robustness.
- Contributes research software to open-source projects and keeps experiments reproducible.
- Publishes and presents results at leading conferences and journals, and contributes to program reports.
- Works with collaborators across institutions.
Requirements
- Ph.D. (completed within the last 0–5 years) in optimization for machine learning, deep learning theory, or distributed or federated learning
- Nice to have: stochastic optimization
- Nice to have: heavy-tailed noise
- Nice to have: analysis of loss landscapes
- Nice to have: generalization