Postdoctoral Appointee - Federated Learning and Foundation Model

Argonne National Laboratory • Lemont, IL

6d • $72,879 - $121,465

About The Position

Argonne National Laboratory’s Mathematics and Computer Science Division is hiring a postdoctoral appointee to research the theory and algorithms underlying stochastic-gradient training and foundation models, especially for scientific data that cannot leave the site where it was collected. The work spans mathematical analysis and the development of efficient, reliable, and private training methods for distributed and federated settings.

The appointee will collaborate with universities and other national laboratories and have access to powerful computing systems. Appointees are encouraged to develop their own research direction, publish at leading machine learning and optimization venues, lead papers, and contribute to open-source software. The role also includes mentoring students and developing professional skills in communication, networking, and leadership.

Requirements

• Ph.D. in a relevant field, completed within the last 0–5 years.
• Publication record in optimization for machine learning, deep learning theory, or distributed or federated learning.
• Strong programming skills in Python and PyTorch, including experience training deep learning models on GPUs.
• Coursework or research experience in optimization, probability, or machine learning.
• Clear communication skills and ability to work on a team spread across several institutions.
• Ability to model Argonne’s core values of impact, safety, respect, teamwork, and integrity.

Nice To Haves

• Background in stochastic optimization, heavy-tailed noise, or analysis of loss landscapes and generalization.
• Experience with federated learning, differential privacy, or other privacy-preserving methods.
• Experience fine-tuning or pretraining foundation models, including parameter-efficient methods such as LoRA (low-rank adaptation).
• Experience with multi-node distributed training, such as DeepSpeed, PyTorch FSDP, or MPI.
• Contributions to open-source machine learning software.

Responsibilities

• Conduct research on optimization and training dynamics for large-scale machine learning, combining analysis with careful experiments.
• Design, implement, and benchmark training algorithms for distributed and federated settings, considering efficiency, privacy, and robustness.
• Contribute research software to open-source projects and maintain reproducible experiments.
• Publish and present results at leading conferences and journals, and contribute to program reports.
• Collaborate with partners across institutions.
• Help mentor students.
• Develop research independence and professional skills such as communication, networking, and leadership.

Benefits

• Comprehensive benefits are part of the total rewards package; specific benefits are not listed in the posting.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: Ph.D. or professional degree