Postdoctoral Appointee - Foundation Models with Federated Learning

Core responsibilities include:

• 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.

This role advances foundation model methodologies for scientific and engineering applications, with a focus on privacy-preserving federated learning, contributing to AI for science and enabling collaborative, interdisciplinary research with positive societal impact.

Required skills, experience and qualifications:

• 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.

Desired skills:

• 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.

The expected hiring range for this position is $72,879.00-$121,465.00. Comprehensive benefits are part of the total rewards package, including healthcare insurance. Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation, with core values of impact, safety, respect, integrity and teamwork.

Argonne National Laboratory is a U.S. Department of Energy multidisciplinary science and engineering research center, committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation.