Postdoctoral Appointee - Federated Learning for Foundation Models - Military Veterans

The Mathematics and Computer Science Division (MCS) at Argonne National Laboratory is seeking a Postdoctoral Appointee to conduct cutting-edge research in machine learning, with a focus on the efficient training and deployment of foundation models in a federated learning framework. The Postdoctoral Appointee will work on the development and optimization of federated learning techniques to enable the training of large-scale foundation models across distributed clients, addressing key challenges such as data heterogeneity and communication efficiency.

The appointee will contribute to the conceptual framework, design, and implementation of federated learning architectures, with a particular emphasis on improving model performance, scaling across distributed systems, and ensuring privacy and security in data handling.

Position Requirements

Required skills and qualifications:

• Ph.D. (completed within the past 0-5 years) in computer science, statistics, data science, applied mathematics, operational research, or a related field.
• Proficiency in coding with Python and experience in C, C++, or other comparable languages.
• Strong background in machine learning techniques and familiarity with ML frameworks such as PyTorch, Jax, or TensorFlow.
• Proven ability to collaborate effectively with scientists, divisions, and external institutions, including universities and national laboratories.
• Excellent oral and written communication skills for engaging with all levels of the organization.
• Ability to model Argonne's core values of impact, safety, respect, impact, and teamwork.

Preferred skills and qualifications:

• Experience with federated learning, particularly in the context of training or deploying foundation models.
• Expertise in managing large-scale training datasets using GPU-enabled computing.
• Familiarity with privacy-preserving machine learning techniques.
• Experience with distributed computing, scaling machine learning models, or handling heterogeneous datasets.
• Knowledge of continual learning frameworks and strategies.
• A strong foundation in statistical methods, optimization, or game theory is a plus.

Job Family
Postdoctoral Family

Job Profile
Postdoctoral Appointee

Worker Type
Long-Term (Fixed Term)

Time Type
Full time

As an equal employment opportunity and affirmative action employer, and in accordance with our core values of impact, safety, respect, integrity and teamwork, Argonne National Laboratory is committed to a diverse and inclusive workplace that fosters collaborative scientific discovery and innovation. In support of this commitment, Argonne encourages minorities, women, veterans and individuals with disabilities to apply for employment. Argonne considers all qualified applicants for employment without regard to age, ancestry, citizenship status, color, disability, gender, gender identity, gender expression, genetic information, marital status, national origin, pregnancy, race, religion, sexual orientation, veteran status or any other 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.