Postdoctoral Research Associate - Federated Learning

Requisition Id 15510 

Overview:

Do you have a passion for applying artificial intelligence (AI) methods for accelerating scientific discoveries and an ability to think outside of the box in a collaborative and open environment? If so, the Oak Ridge National Laboratory’s Learning Systems Group within the Data and Artificial Intelligence Systems section invites you to apply to our new postdoctoral research associate position in AI for science.

The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models across multiple institutions. This position offers a unique opportunity to work with the world's first exascale system, the Frontier supercomputer, and collaborate with experts in machine learning, optimization, electric grid analytics, and image science. 

The successful candidate will design and implement differential privacy solutions for large-scale scientific data models in federated learning environments. You will advance privacy-preserving machine learning by developing efficient techniques that maintain robust privacy guarantees while minimizing performance impact. Additionally, you will optimize the balance between privacy and utility, addressing the challenges of heterogeneous privacy budgets and varying requirements across diverse clients.

You will use Frontier's computational power to scale and validate these privacy-preserving algorithms, enabling breakthroughs across energy and image modeling domains. You will also collaborate with ORNL's AI initiative to advance secure, trustworthy, and efficient AI for science. This position offers a unique opportunity to make significant theoretical and applied contributions to differential privacy in federated learning, driving advancements in secure, collaborative AI systems globally.

As a postdoctoral researcher, you will help solve some of the most challenging problems faced by the nation. You will perform groundbreaking research on a wide range of significant problems, and you will apply your work to exciting research in multi-disciplinary domains alongside globally recognized experts. You will bring creative thinking, teamwork, and machine learning skills to bear as you develop new methods to address scientific and engineering problems, collaborate with leaders in your field and across the laboratory, while working with the world’s fastest computers, and disseminate innovative results through publications and presentations.

The Learning Systems Group at the Oak Ridge National Laboratory focuses on artificial intelligence and computational research and applies this knowledge to support the nation’s leading initiatives. We hire top researchers who are ready to tackle the hardest problems faced by both the US and the world. Researchers of the Learning Systems group are changing the world with their research, and we are committed to hiring and retaining highly motivated, energetic, and creative individuals that will help us continue our breakthrough scientific endeavors.

Major Duties/Responsibilities:

• Develop and apply differential privacy for large-scale models scientific data to advance research efforts across scientific systems.
• Develop and apply federated learning on distributed and heterogenous datasets.
• Develop more efficient and resilient DP techniques that minimize performance loss while still providing robust privacy guarantees.
• Develop novel privacy-preservation methods that accommodate the diverse privacy requirements of a large number of clients.
• Develop novel mathematically rigorous approaches to optimize the trade-off between privacy and utility especially in the context of large models.
• Advance knowledge of key AI methods such as deep learning, algorithm design, probability theory, privacy definitions, and apply it to develop efficient privacy preserved federated learning model.
• Communicate and coordinate experimental results with other domain experts to facilitate collaboration.
• Present and report research results and publish scientific results in peer-reviewed journals or conferences.
• Build strong collaborations within ORNL and across the global research community.

Basic Qualifications:

• A PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline completed within the last 3 years or to be expected in 2025.
• Demonstrated hands-on experience and understanding of developing and applying privacy preservation methods to ML models.
• Demonstrated research experience with federated learning techniques.
• Demonstrated experience working with machine learning and data analytics using tools in programming languages such as Python, PyTorch, Pandas, Scikit Learn, etc., in applied problem-solving contexts.
• Understanding of machine learning algorithms (gradient descent, random forests, etc.) and deep neural network architectures (ResNet and Transformers).

Preferred Qualifications:

• Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions.
• Knowledge of SOTA federated learning algorithms.
• Knowledge of distributed optimization and consensus algorithms.
• Knowledge of large models and hyper-parameter optimization.
• Knowledge of high-performance computing and its applications.
• An excellent record of productive and creative research, as demonstrated by publications in top peer-reviewed journals.
• Strong problem-solving skills and an interest in interdisciplinary collaboration.
• Commitment to open-source principles and scientific transparency.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers.
• Ability to set priorities to accomplish multiple tasks within deadlines and adapt to changing needs.

Special Requirements:

Applicants cannot have received their Ph.D. more than five years prior to the date of application and must complete all degree requirements before starting their appointment. The appointment length will be up to 24 months with the potential for extension. Initial appointments and extensions are subject to performance and availability of funding.

Please submit three letters of reference when applying to this position. You may upload these directly to your application or have them sent to recruiting@ornl.gov with the position title and number referenced in the subject line.

Instructions to upload documents to your candidate profile:

• Login to your account via jobs.ornl.gov
• View Profile
• Under the My Documents section, select Add a Document

Security, Credentialing, and Eligibility Requirements:

• This position requires the ability to obtain and maintain an HSPD-12 PIV badge.
• For employment at Oak Ridge National Laboratory (ORNL), a Real ID compliant form of identification will be required.
• Additionally, ORNL is subject to Department of Energy (DOE) access restrictions. All employees must also be able to obtain and maintain a federal Personal Identity Verification (PIV) card as mandated by Homeland Security Presidential Directive 12 (HSPD-12) and Department of Energy (DOE) Order 473.1A, which requires a favorable post-employment background investigation.
• To obtain this credential, new employees must successfully complete and pass a Federal Tier 1 background check investigation. This investigation includes a declaration of illegal drug activities, including use, supply, possession, or manufacture within the last year. This includes marijuana and cannabis derivatives, which are still considered illegal under federal law, regardless of state laws.

For foreign national candidates:

• If you have not resided in the U.S. for three consecutive years, you are not eligible for the PIV credential and instead will need to obtain a favorable Local Site Specific Only (LSSO) risk determination to maintain employment.
• Once you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment.

Benefits at ORNL:

ORNL offers competitive pay and benefits programs to attract and retain talented people! The laboratory offers many employee benefits, including medical and retirement plans and flexible work hours, to help you and your family live happy and healthy. Employee amenities such as on-site fitness, banking, and cafeteria facilities are also provided for convenience.

Other benefits include the following: Prescription Drug Plan, Dental Plan, Vision Plan, 401(k) Retirement Plan, Contributory Pension Plan, Life Insurance, Disability Benefits, Generous Vacation and Holidays, Parental Leave, Legal Insurance with Identity Theft Protection, Employee Assistance Plan, Flexible Spending Accounts, Health Savings Accounts, Wellness Programs, Educational Assistance, Relocation Assistance, and Employee Discounts.

If you have difficulty using the online application system or need an accommodation to apply due to a disability, please email: ORNLRecruiting@ornl.gov.

This position will remain open for a minimum of 5 days after which it will close when a qualified candidate is identified and/or hired.

We accept Word (.doc, .docx), Adobe (unsecured .pdf), Rich Text Format (.rtf), and HTML (.htm, .html) up to 5MB in size. Resumes from third party vendors will not be accepted; these resumes will be deleted and the candidates submitted will not be considered for employment.

If you have trouble applying for a position, please email ORNLRecruiting@ornl.gov.

ORNL is an equal opportunity employer. All qualified applicants, including individuals with disabilities and protected veterans, are encouraged to apply.  UT-Battelle is an E-Verify employer.