10 context-aware Postdoctoral positions at Oak Ridge National Laboratory in United States
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completed within the last 5 years or expected to be completed soon. Expertise in AI/ML methods in an environmental or Earth science context Experience programming in Python or related languages A strong
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sensor data in a materials science context. An excellent record of productive and creative research demonstrated by publications in peer-reviewed journal papers. Excellent written and oral
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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
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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avoidance, autonomous exploration, frontier selection, and localization-aware trajectory planning. Develop high accuracy point cloud registration and mapping workflows. Plan and conduct experiments
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, including automated QC and uncertainty-aware learning from sparse/noisy measurements Build hybrid mechanistic–AI models linking traits to photosynthesis, stomata, hydraulics, and respiration across
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characterization, and predictive fault tolerance in HPC systems. Architectural exploration and performance modeling of high-bandwidth memory (HBM) and DDR memory systems in the context of data-intensive scientific
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to the development of scalable, explainable, and uncertainty-aware AI methods that enhance model robustness, reliability, and scientific discovery. Publish research findings in high-impact journals and present results
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
. Focus will largely be in developing and deploying such AI/ML algorithms, closely collaborating with theorists and experimentalists to realize physics- models and/or physics-aware ML-models that can bridge