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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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algorithms, capable of distributed learning on high performance and edge computing; The design of architectures/models which accurately capture the complexities of the data, with robust estimates of confidence
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system integration, as well as embedded controller development. Experience in modeling converter topologies and distribution grids, developing control algorithms, and implementing controller code is
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to earthquakes, landslides, and volcanic activity. In addition to model development, research activities include the design of evaluation protocols that capture domain knowledge and sponsor application
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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
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infrastructures using GPU accelerators. Experience building data-fusion workflows to ingest multi-modality geospatial data. Experience using Python or other programming languages to develop AI algorithms in PyTorch