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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 17 hours ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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) on the development of advanced Pediatric MR research with a primary focus on the brain and/or heart (e.g., MR spectroscopic imaging, MR elastography, susceptibility mapping and/or dynamic imaging). The candidate will
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of the US Genesis Mission at BNL, aimed among others at accelerating the quantum information efforts in the Nation. The mission is to address challenges facing scalable quantum computing and to develop novel
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research knowledge by participating in educational opportunities; reading professional publications; maintaining personal networks; participating in professional organizations. · Use and continually develop
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working at University of Houston More Jobs from This Employer https://main.hercjobs.org/jobs/21900191/post-doctoral-fellow-big-data-algorithms-group Return to Search Results
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. The mission is to address challenges facing scalable quantum computing and to develop novel and improved platforms for quantum computation and communication and thus strengthen U.S. leadership in QIST
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to develop and translate advanced computational tools that directly impact patient care in a clinical environment. Responsibilities: Develop GPU-accelerated dose calculation and optimization algorithms
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for large-scale and distributed systems. The role focuses on developing and analyzing algorithms, building reproducible computational studies, and disseminating results in leading venues. The position
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intersection of human-robot interaction and manipulation in healthcare settings. The project involves developing algorithmic solutions for an optometry application where robots assist optometry patients
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-series modeling, and clustering algorithms. The candidate is expected to lead an effort to prepare generalized ML techniques for data quality monitoring for tasks across multiple HEP experiments