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.…