Postdoctoral Research Associate - Privacy Preserved Federated Learning Algorithms

Oak Ridge National Laboratory • Oak Ridge, TN

108d • Onsite

About The Position

Oak Ridge National Laboratory (ORNL) is seeking a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms for its Discrete Algorithms Group. The role involves developing cutting-edge differential privacy techniques for large-scale models across multiple institutions, with opportunities to work with 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, aiming to advance privacy-preserving machine learning by developing efficient techniques that minimize performance impact while maintaining robust privacy guarantees. This includes optimizing the balance between privacy and utility and addressing challenges related to heterogeneous privacy budgets and varying client requirements.

Requirements

• A PhD in Computer Science, Applied Mathematics, Computational Science, or related discipline.
• Demonstrated hands-on experience and understanding of developing and applying privacy preservation methods to ML models.
• Demonstrated research experience with AI and ML techniques.
• 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.
• A Real ID compliant form of identification will be required.
• Must be able to obtain and maintain a federal Personal Identity Verification (PIV) card.
• Must successfully complete and pass a Federal Tier 1 background check investigation.
• Must declare illegal drug activities, including use, supply, possession, or manufacture within the last year (including marijuana and cannabis derivatives, which are illegal under federal law).

Nice To Haves

• Knowledge of Approximate, Local, Rényi, Bayesian differential privacy, and other related definitions.
• Knowledge of federated learning SOTA 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.
• Motivated self-starter with the ability to work independently and to participate creatively in collaborative and frequently interacting teams of researchers.

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.

Benefits

• 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
• Employee Discounts
• medical and retirement plans
• flexible work hours
• on-site fitness
• banking
• cafeteria facilities

What This Job Offers

• Job Type: Full-time
• Career Level: Entry Level
• Education Level: Ph.D. or professional degree