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national security, proliferation detection, and nuclear forensics applications. This position resides in the Collection Science and Engineering Group in the Material Characterization and Modeling Section
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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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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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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
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methods to work with a team of scientists in CSD to model chemical reactions important to determine the longevity of amorphous materials. That mechanistic information will be incorporated into process-based
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such as quantization, model pruning, approximate attention (linear and sparse) and proposing new mechanisms for tackling speed, accuracy, as well as energy issues, for large language mode (LLM) inferencing
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for simulating atomic nuclei, as well as preparing data and using machine learning models for investigating how the properties of atomic nuclei connect to fundamental questions in physics, such as constraining
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materials that may serve as model systems displaying quantum behaviors. It will also provide opportunities for collaboration with quantum computing efforts within the Quantum Science Center, guiding and
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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
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Requisition Id 15625 Overview: We are seeking a Postdoctoral Research Associate to advance modeling and AI-driven analysis for magnetic quantum materials, with a focus on neutron scattering and