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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
challenges facing the nation. ORNL offers competitive pay and benefits programs to attract and retain individuals who demonstrate exceptional work behaviors. The laboratory provides a range of employee
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, artificial intelligence and machine learning, data management, workflow systems, analysis and visualization technologies, programming systems and environments, and system science and engineering. Major Duties
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with environment, safety, health and quality program requirements. Maintain strong dedication to the implementation and perpetuation of values and ethics. Deliver ORNL’s mission by aligning behaviors
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, safety, health, and quality program requirements. Uphold strong values and ethics in collaborative research. Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values
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data analytics using tools in programming languages such as Python, PyTorch, Pandas, Scikit Learn, etc., in applied problem-solving contexts. Understanding of machine learning algorithms (gradient
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the development of AI architecture for holistic genomic photosynthesis modeling. Evaluate performances of AI genomic photosynthesis models. Report advances to program management and broader scientific communities
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completed within the last 5 years or to be expected in 2025. Demonstrated experience working with machine learning and data analytics using tools in programming languages such as Python, PyTorch, Pandas
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systems, scalable algorithms and systems, artificial intelligence and machine learning, data management, workflow systems, analysis and visualization technologies, programming systems and environments, and
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Department of Veterans Affairs (VA). As such, you will have the opportunity to work on some of the most challenging and impactful research and development programs in healthcare informatics, bioinformatics
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applications. You’ll help design, train, and evaluate AI systems that plan, reason, and take actions to accelerate scientific discovery across domains (materials, chemistry, climate, fusion, biology, and more