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Learning skills. This position resides in the AI Operations Program office within the Application Development Division of the Information Technology Services Directorate. Our AI/ML models are heavily
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in multiscale and multifidelity simulation techniques (ab initio methods at different fidelity, machine learning tight-binding, machine learning force fields, phase-field modeling, and/or kinetic monte
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, designing and testing solutions, or similar experience or equivalent combination of education and experience will be considered Demonstrated ability to learn and support multiple business areas Ability
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to numerical methods for kinetic equations. Mathematical topics of interest include high-dimensional approximation, closure models, machine learning models, hybrid methods, structure preserving methods, 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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National Laboratory (ORNL). This is an opportunity to develop novel tools and techniques across scales (river basin to national), publish in top-tier journals, collaborate with industry and academic partners
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safety practices. Foster a collaborative, team-oriented work environment. Basic Qualifications: High School Diploma or equivalent. 2-4 years of relevant administrative, front desk, customer service
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, substation, corridor scenarios) Integrate physics-informed machine learning models with signal processing feature extraction Develop prototype software tools for automated waveform analytics and real-time
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, assessing hazards for every task, and committing to continuous learning. Other tasks as assigned by management. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values
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multiple types of sensing modalities, where this expertise is applied to solve critical national problems in energy and security. Demonstrated knowledge of emerging AI and machine learning techniques as