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Postdoctoral Research Associate- AI/ML Accelerated Theory Modeling & Simulation for Microelectronics
Requisition Id 15685 Overview: The Center for Nanophase Materials Sciences (CNMS) is seeking a Postdoctoral Research Associate to support research directed towards developing novel AI/ML algorithms
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research associate position in AI for science. The Learning Systems Group seeks a postdoctoral researcher specializing in federated learning and privacy-preservation algorithms. The successful candidate will
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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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industrial imaging data. You will directly contribute to developing and deploying algorithms for multi-modal tomography (X-ray, neutron, and electron), advancing methods for non-destructive evaluation (NDE
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travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and analysis necessary for simulating and understanding
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uncertainty quantification. The position comes with a travel allowance and access to advanced computing resources. The MMD group is responsible for the design and development of numerical algorithms and
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for the design and development of numerical algorithms and analysis necessary for simulating and understanding complex, multi-scale systems. The group is part of the Mathematics in Computation (MiC) Section
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the Integrated Building Deployment and Analysis Group in the BTSD, ESTD at Oak Ridge National Laboratory (ORNL). The IBDA group leads the development of innovative methods for residential and commercial whole
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, water, carbon, and materials productivity throughout the U.S. economy and to identify opportunities for improvement. Through the Industrial Energy Efficiency Program, the MEERA Group develops a diverse
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. Eventually, we aim to map these algorithms on to energy-efficient emerging devices. In addition, you may also explore applying LLMs to drive multimodal models in scientific domains towards deep reasoning. As a