Postdoctoral Appointee - Foundation Models with Federated Learning

Argonne National Laboratory • Lemont, IL

4d

About The Position

We are seeking a highly motivated postdoctoral researcher to conduct independent research on foundation models for scientific and engineering applications, with an emphasis on training, adaptation, and evaluation in distributed and privacy-aware settings. While the position is supported by an AI for Science project on privacy-preserving federated learning, the broader objective is to advance foundation model methodologies, with federated learning serving as a key enabling research direction. The postdoctoral researcher will be advised by the principal investigator, while being expected to exercise increasing independence in defining research problems, developing methodologies, and driving publications. The role values strong research judgment and analytical thinking, complemented by effective use of modern AI tools to accelerate the entire research workflow—including literature exploration, experiment design, implementation, analysis, and dissemination. The researcher will work in a collaborative, interdisciplinary environment with access to large-scale computing resources and diverse scientific use cases. The position strongly supports publishing in top-tier venues, contributing to open-source research artifacts, and developing an independent research agenda in AI for science.

Requirements

• PhD in computer science, applied mathematics, electrical engineering, statistics, or a closely related field, completed within the last 0–5 years is required.
• Demonstrated ability to conduct independent research, including problem formulation, methodological development, and publication in peer-reviewed venues.
• Strong background in machine learning, with research experience in deep learning, foundation models, or related areas.
• Solid programming ability in Python and experience with modern ML frameworks (e.g., PyTorch or equivalent), sufficient to support research and experimentation.
• Ability to effectively leverage modern AI tools to improve research productivity across the full research lifecycle.
• Strong written and oral communication skills, with the ability to publish research in peer-reviewed venues.
• Ability to model Argonne's core values of impact, safety, respect, integrity and teamwork.

Nice To Haves

• Prior research experience in federated learning, distributed learning, or privacy-preserving machine learning.
• Experience with large-scale model training or analysis of scaling behavior.
• Familiarity with challenges such as data heterogeneity, communication efficiency, or system constraints.
• Exposure to privacy, robustness, or security techniques (e.g., differential privacy, secure aggregation).
• Experience contributing to open-source research software.

Responsibilities

• Leading research on foundation models, including problem formulation, algorithmic development, and rigorous experimental evaluation.
• Advancing federated learning methods that enable distributed and privacy-aware training and adaptation of foundation models.
• Using modern AI tools to accelerate research productivity across ideation, coding, experimentation, analysis, and writing.
• Interpreting results critically and positioning contributions within the broader research literature.
• Publishing research outcomes and contributing to reusable research software when appropriate.

Benefits

• comprehensive benefits are part of the total rewards package.
• Click here to view Argonne employee benefits!

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree
• Number of Employees: 1,001-5,000 employees