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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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and automated fault detection and diagnosis (AFDD) algorithms to buildings Deliver ORNL’s mission by aligning behaviors, priorities, and interactions with our core values of Impact, Integrity, Teamwork
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, high-performance computing (HPC), and computational sciences. Major Duties/Responsibilities: Participate in: (1) design and implementation of scalable DL algorithms for atomistic materials modeling
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data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large-scale multimodal neuroimaging dataset, brain
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learning on large-scale HPC systems Scalable and energy-efficient AI training algorithms Image reconstruction, segmentation, and spatiotemporal modeling High-performance computing for large-scale AI and
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on creating innovative artificial intelligence algorithms for the trusted visualization of large-scale 3D scientific data. This position resides in the Data Visualization Group in the Data and AI Systems
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. Essential Duties and Responsibilities Neuroimaging data collection and management Data analysis and model building Develop advanced deep learning and machine learning algorithms. Assist with organizing large
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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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. We have multiple NIH-funded projects to study the influence of FDA-approved GLP-1R agonists on drug seeking and reinstatement in rodents. A newly funded NIH R01 has the goal to determine who may be
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atmospheric remote sensing from ground, airborne, and satellite platforms. Our group develops advanced algorithms and data analysis methods to address fundamental scientific challenges, including global cloud