68 machine-learning "https:" "https:" "https:" "U.S" research jobs at Oak Ridge National Laboratory
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computational physics, computational materials, and machine learning and artificial intelligence, using the DOE’s leadership class computing facilities. This position will utilize methods such as finite elements
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Expertise in machine learning and big data analysis Excellent written and oral communication skills Motivated self-starter with the ability to work independently and to participate creatively in collaborative
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advanced many-body methods, high-performance computing, and machine learning approaches. The successful candidate will play a leading role in developing computational methods and high-performance algorithms
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of the ORNL scientific community, you will be expected to commit to ORNL's Research Code of Conduct. Our full code of conduct and a statement by the Lab Director's office can be found here: https
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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
ML concepts and architectures and hands-on experience with open-source AI/ML packages (such as pytorch, scikit-learn, tensorflow, JAX etc.). Preferred Qualifications: Good grasp of concepts in solid
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elemental Hg. Acquire and analyze data using a range of analytical instrumentation. Maintain detailed and accurate records. Prepare oral and written reports. Publish and present research results in peer
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. Additional information regarding ORNL’s DSF Program may be found by visiting the following link: https://www.ornl.gov/careers/distinguished-fellowships . Security, Credentialing, and Eligibility Requirements
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to commit to ORNL’s Research Code of Conduct. Our full code of conduct and a statement by the Lab Director’s office can be found here: https://www.ornl.gov/content/research-integrity Basic Qualifications
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to Computational Fluid Dynamics. Mathematical topics of interest include structure-preserving finite element methods, advanced solver strategies, multi-fluid systems, surrogate modeling, machine learning, 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