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Field
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incumbent will conduct research within the predetermined research scope and methodology, and conduct research in the fields of quantum-engineered power grids and AI-enabled smart grids. ● Develop
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 22 hours ago
into FE codes for application in process optimization studies Utilize ML algorithms and optimization frameworks in conjunction with FE simulations to develop process optimization framework for metal/ceramic
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validate signal processing algorithms and stimulation strategies using electrophysiological and behavioral data. Develop GUIs, psychophysical test protocols, and objective outcome measures (e.g., ECAP, ABR
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methodologies in brain diseases. The candidate will work on developing advanced new algorithms, testing and validation, and applications in these data modalities. The candidate will have the opportunity to work
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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
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projects that target NERSC HPC systems. Analyze and optimize quantum-related workflows, including classical simulation of quantum systems. Develop and apply advanced workflow capabilities such as on-system
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properties of macromolecules, developing novel ways to combine quantum chemical methods and machine learning, developing quantum algorithms for computational chemistry on quantum computers, and applying
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: Contribute to one or more NESAP projects that target NERSC HPC systems. Analyze and optimize quantum-related workflows, including classical simulation of quantum systems. Develop and apply advanced workflow
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 18 hours ago
and generative models. Develop novel algorithms for generative modeling tasks and optimize LLM/GPT-like models on large datasets. Stay abreast of advancements in language modeling and generative AI
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leading peer-reviewed journals and conferences. Researching and developing parallel/scalable uncertainty visualization algorithms using HPC resources. Collaboration with domain scientists for demonstration