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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
for model refinement. Perform multi-scale simulations (e.g. DFT / atomistic / phase-field simulations) to train AI/ML models. Conduct scientific research on ferroelectrics and/or 2D memristive materials
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strategic analysis and modeling research activities of advanced materials, supply chain, and advanced manufacturing technologies for various DOE program offices. The latest analyses focus on DOE’s Advanced
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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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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
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to Computational Methods for Data Reduction. Topics include data compression and reconstruction, data movement, data assimilation, surrogate model design, and machine learning algorithms. The position comes with a
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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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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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chain, material flows, and development costs throughout the lifecycle of a geothermal well to identify opportunities to reduce materials, time, and environmental impact of geothermal wells. Help support
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of agentic AI for science, scientific reasoning, federated & collaborative learning, and reinforcement learning (RL) for self-improving models, in the context of leadership scientific workflows and
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photosynthesis to join the new pilot study of Generative Pretrained Transformer for genomic photosynthesis (GPTgp). The GPTgp project aims to develop a foundational holistic model of photosynthesis that will scale