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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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AI/ML surrogate models for inverse design of new materials and processes, incorporating simulated and experimental multi-modal datasets. Develop AI/ML approaches to bridge length- and time-scales in
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post-doctoral research associate to simulate amorphous materials and crystallization reactions using atomic-scale simulations. As a post-doc, you will utilize high performance computing and rare event
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characterization, mechanical testing, 3D microstructural analysis, finite element simulations, atomistic modeling, and thermal transport measurement techniques to advance mechanistic understanding and predictive
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relationships between data and metadata. Collaborate on innovative solutions to automate and optimize the interplay between large scientific simulations, data ingestion, and AI processes (e.g., model training
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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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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computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding