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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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National Aeronautics and Space Administration (NASA) | Cleveland, Ohio | United States | 20 minutes ago
Postdoctoral Program website for application instructions and requirements: How to Apply | NASA Postdoctoral Program (orau.org) A complete application to the NASA Postdoctoral Program includes: Research proposal
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: Computational Sciences / Bio-computing Appl Deadline: (posted 2025/11/10, listed until 2025/11/30) Position Description: Apply 2025/11/30 11:59PM Position Description As a computing researcher, you will be
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of Program & Employment Compliance at compliance@uada.edu . For general application assistance or if you have questions about a job posting, please contact Human Resources at 501-671-2219 or 479-502
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Oak Ridge National Laboratory, Mathematics in Computation Section Position ID: ORNL-POSTDOCTORALRESEARCHASSOCIATE5 [#27233] Position Title: Position Location: Oak Ridge, Tennessee 37831
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distributed quantum computing. The center includes other quantum faculty, and conducts a wide range of collaborative quantum research in the areas of quantum computing, quantum algorithms and complexity
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implements machine and deep learning programs. Develops algorithms to deconvolve RNA-seq data and compare them to AI-based methods. Performs follow up validation efforts on cell lines. Minimum Qualifications
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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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Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments. Modeling dynamical systems
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for the Pitkow Lab. Core Responsibilities Include: Develop computational methods for inference and control that improve the reliable and efficient operation of autonomous agents in complex, uncertain environments