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specifically on developing machine learning-based surrogates and emulators for the dynamics of power grids. This role involves creating advanced probabilistic models that capture the complex behaviors
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on integrating and analyzing diverse data types to support scientific discovery Research and implement data management solutions using advanced storage systems Evaluate and optimize AI applications for performance
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, and optimize for energy efficiency HPC applications and high performance data stream analytics workloads. Use of novel accelerator designs, and automatic methods to model/predict how performance would
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-micron resolution of entire organs demands optimized data pipelines and methods to handle and visualize the resulting datasets. Additionally, beamline hardware must be optimized to ensure the highest data
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for deployment on large-scale computing resources, such as high-performance computers (e.g., Perlmutter, Aurora, etc.). This includes tasks such as automating model design, optimizing hyperparameters, and training
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their environmental impacts including energy use, water use, emissions, and resource depletion. Coursework and some project experience in application of mathematical optimization, statistics, or machine
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We are seeking a highly motivated Postdoctoral Appointee with a strong background AI/ML specifically in the development and application of Large Language Models (LLMs) tailored for scientific use
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such as PyTorch and TensorFlow. Experience with high-performance computing and/or scientific workflow. Strong background in inverse problems, numerical optimization and image processing. Job Family
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, energy, and critical materials supply chains. In this role, you will be responsible for analyzing existing and future supply chains, with a specific focus on understanding the complexities and challenges
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of scientists and High-Performance Computing (HPC) engineers. In the AL/ML group, we work at the forefront of HPC to push scientific boundaries, carrying out research and development in state