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                The University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago, and medicine. Key Responsibilities Collaborate with researchers to design, develop, and refine large language and generative models. Develop novel algorithms for generative modeling tasks and optimize 
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                Massachusetts Institute of Technology | Cambridge, Massachusetts | United States | about 2 months ago. The work will apply state-of-the-art three-dimensional atmospheric chemistry and circulation models, together with advanced statistical techniques (optimal Bayesian, Markov Chain-MonteCarlo, etc.) to solve 
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                University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 22 hours agoof materials Proficiency in multi-physics FE simulations, topology optimization, and working knowledge of machine learning (ML) algorithms Demonstrated exceptional problem-solving abilities, a passion for 
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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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                and/or interest in one or more of the following: optimization and optimal control (esp. optimal control of partial differential equations (PDE) or PDE-constrained optimization), scientific machine 
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                on optimizing the use of digital health technologies to better serve the needs of end users, including both patients and healthcare providers. This role offers an exciting opportunity to contribute to impactful 
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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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                resources, and the DOE ESNET network. Develop and apply advanced workflow capabilities that improve performance, portability, and productivity. Perform performance analysis and optimization across end-to-end 
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                computational research in accelerator science and technology. The focus is on developing and applying machine learning (ML) methods for accelerator operations and beam-dynamics optimization in advanced 
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                optimization under uncertainty, constrained query evaluation, or the design of efficient, explainable, and scalable query engines. The successful applicant will help design and build novel systems and algorithms