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Engineering, or other field relevant to the job duties. Competency in Python and C++ programming. A commitment to lifelong learning. Minimum of 5 years of experience relevant to the job duties listed Preferred
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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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can lead computational and experimental activities using commercial codes to compare with experiments and also be able to develop new computational models and computer programs. The successful candidate
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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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a focus on multimodal learning, computer vision, and scientific machine learning Develop novel algorithms and architectures for tasks such as multimodal retrieval, reasoning over complex data, and
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of computational scientists, applied mathematicians, and computer scientists to link models and algorithms with high-performance computing. Author peer reviewed papers for internal and external release as
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data processing and multigroup cross-section generation tools such as AMPX or NJOY. Experience applying artificial intelligence, machine learning, or surrogate modeling methods to nuclear engineering or
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, geographers, mathematicians, physicists, computer scientists, and engineers – in research, development, integration, testing, and deployment. Basic Qualifications: Requires an B.S. or M.S. in the field
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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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National Laboratory (ORNL). This group leads the application of machine and cascade theory to develop, execute, and interpret testing evolutions for discovery, performance evaluation, and optimization