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analytics, including correlation analysis and machine learning techniques. Preferred Qualifications: Experience with microstructure characterization techniques (SEM, EBSD, TEM, XRD). Experience in mechanical
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solutions, and making sound decisions without requiring extensive day-to-day supervision. Radiochemistry and/or nuclear chemistry experience is preferred but not required; willingness and ability to learn
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Research Associate to develop, scale, and apply artificial intelligence (AI) and deep learning (DL) models for power grid systems. The successful candidate will contribute to scalable AI workflows for grid
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),. Familiarity with data analytics, machine learning, digital twin knowledge, or Python programming language. Knowledge of additive manufacturing, process physics, thermodynamics, and/or metallurgy to interpret
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Requisition Id 16802 Overview: We are seeking a Postdoctoral Research Associate for the development and application of advanced multiphysics simulations, and machine learning (ML) methods relevant
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seeking a postdoctoral researcher with expertise in data management, workflow management, High Performance Computing (HPC), machine learning and Artificial Intelligence to enhance our capabilities in making
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monitoring in manufacturing environment Develop modular, extensible workflows for data processing Develop and deploy data analytics, machine learning, and statistical modeling methods for multimodal
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-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
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Requisition Id 16723 Overview: We are seeking a Postdoctoral Research Associate with expertise in artificial intelligence (AI) and machine learning (ML) for multiscale physical systems
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. Experience with developing machine-learning surrogates for structure-property relationship, generative AI models, material representations, machine learning force-fields (especially extensions to spinful