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Oak Ridge National Laboratory · 23 hours ago

Postdoctoral Research Associate - Federated Learning

Tennessee (TN)

Full-time

Onsite

New Grad, Entry Level

Oak Ridge National Laboratory is seeking a postdoctoral research associate specializing in federated learning and privacy-preservation algorithms. The successful candidate will develop cutting-edge differential privacy techniques for large-scale models and collaborate with experts in various domains to drive advancements in secure AI systems for scientific research.

Advanced MaterialsClean EnergyEnergyEnergy ManagementManufacturingNuclearRenewable Energy

No H1B

U.S. Citizen Only

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

Qualification

Federated learningDifferential privacyMachine learningPythonHigh-performance computingInterdisciplinary collaborationProblem-solvingCreative thinkingTeamwork

Required

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

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