Postdoctoral Appointee - Federated Learning and Foundation Model

Foundation models are changing how science is done, but the way they learn is still poorly understood, and training them on scientific data runs into real limits: scale, cost, and data that can’t leave the site where it was collected. Our group at Argonne National Laboratory works on the methods underneath: the mathematics of how stochastic gradient training behaves, and algorithms that make training efficient, reliable and private across distributed and federated settings. We are looking for a postdoctoral appointee to join this work, from theory through to open-source software that others use.

This Postdoctoral Appointee will be part of the Mathematics and Computer Science Division and will work with partners at universities and other national laboratories and have access to some of the world’s most powerful computing systems. Appointees are encouraged to shape their own research direction and publish at leading machine learning and optimization venues. Postdoctoral appointees are expected to lead papers.

Core 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.
• Postdoctoral appointees also help mentor students.
• Grows research independence and professional skills such as communication, networking and leadership.

Position Requirements Required skills:

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