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: Ph.D. (completed within the last 0–5 years) in one of the fields above, with a publication record in optimization for machine learning, deep learning theory, or distributed or federated learning Strong programming skills in Python and PyTorch, including training deep learning models on GPUs Coursework or research experience in optimization, probability or machine learning Clear communication and the ability to work in a team spread across several institutions Ability to model Argonne's core values of impact, safety, respect, teamwork and integrity Desired skills (nice to have, not required): Background in stochastic optimization, heavy-tailed noise, or the 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 like LoRA (low-rank adaptation) Experience with multi-node distributed training (for example DeepSpeed, PyTorch FSDP or MPI) Contributions to open-source machine learning software Job Family Postdoctoral Job Profile Postdoctoral Appointee Worker Type Long-Term (Fixed Term) Time Type Full time The expected hiring range for this position is $72,879.00-$121,465.00. Please note that the pay range information is a general guideline only. The pay offered to a selected candidate will be determined based on factors such as, but not limited to, the scope and responsibilities of the position, the qualifications of the selected candidate, business considerations, internal equity, and external market pay for comparable jobs. Additionally, comprehensive benefits are part of the total rewards package. Click here to view Argonne employee benefits! As an equal employment opportunity employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a safe and welcoming workplace that fosters collaborative scientific discovery and innovation. Argonne encourages everyone to apply for employment. Argonne is committed to nondiscrimination and considers all qualified applicants for employment without regard to any characteristic protected by law. Argonne employees, and certain guest researchers and contractors, are subject to particular restrictions related to participation in Foreign Government Sponsored or Affiliated Activities, as defined and detailed in United States Department of Energy Order 486.1A. You will be asked to disclose any such participation in the application phase for review by Argonne's Legal Department. All Argonne offers of employment are contingent upon a background check that includes an assessment of criminal conviction history conducted on an individualized and case-by-case basis. Please be advised that Argonne positions require upon hire (or may require in the future) for the individual be to obtain a government access authorization that involves additional background check requirements. Failure to obtain or maintain such government access authorization could result in the withdrawal of a job offer or future termination of employment. Interested in translating science into innovation? Build your career at Argonne. At Argonne, we view the world from a different perspective. Our scientists and engineers conduct world-class research in clean energy, the environment, technology, national security and more. We’re finding creative ways to prepare the world for a better future. To learn more about Argonne's benefits programs, amenities, and other employee programs, visit our main Careers site. To learn more about the exciting science going on at Argonne, visit our Science and Technology page. To request a reasonable accommodation or for other application support, contact us anytime at [email protected] or 630-252-2336.

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