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Postdoctoral Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI
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fellow will develop and apply computational and machine-learning approaches to problems in functional genomics and genome engineering. Research areas may include: Developing machine-learning and deep
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: Research and development of scalable AI, ML, and deep learning models using the Oak Ridge Leadership Computing Facility (OLCF) systems. Conduct research with scalable transformer-based foundation models with
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, geometric deep learning, operator learning, generative models, or AI agents. Knowledge of HPC matrix, tensor and graph algorithms. Knowledge on distributed algorithms using MPI and other frameworks such
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. 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
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-tuning, and integrating human-in-the-loop reinforcement learning approaches. Responsible AI : Exploration of privacy-preserving AI techniques, enhancing AI safety, and addressing vulnerabilities and
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complex challenges in science, energy, national security, and health. Our researchers develop scalable machine-learning models, deep-learning architectures, and data-driven methods for platforms ranging
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learning or deep learning models Ability to present complex results to multidisciplinary teams, including engineering, scientific, and operational stakeholders Ability to work effectively in a dynamic
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have: The ability to independently design and execute experiments and interpret data Expertise in one of the following areas: Deep learning-based microscopy image analysis Molecular and cellular