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and extreme environment structural materials. A strong background in mechanical behavior of materials is required. Demonstrated experience in the implementation of nonlinear constitutive models in
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, a proven publication record, and effective interpersonal skills. Preferred Qualifications: Knowledge of graph neural networks and other geometric deep learning approaches for graph-structured
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pipelines for structured and unstructured data Experience with multimodal datasets (e.g., imaging, time-series, and process data) Experience with API-based data services, workflow automation, or integration
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deposition Complete twisted heterostructure and device fabrication using free-standing oxide membranes for physical property characterizations Data analysis of structural, ferroelectric, electric transport
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and software tools for visualizing and analyzing materials characterization data Develop novel, data-driven materials characterization workflows Advance understanding of process-structure-property
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of NTI and CNMS to develop HPC workflows that can perform multi-fidelity simulations to predict and interpret a wide range of structural and electronic characterization techniques Develop physics-informed
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
-state physics, ferroelectrics and/or 2D materials. Strong background in developing and/or applying materials simulation methods, such as atomistic simulations using electronic-structure and/or machine