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, collaborative work environment that can be deeply rewarding for the right individual. Further information is available at http://www.sci.utah.edu/. Research Focus of This Position This position centers
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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 7 days 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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, retirement plans, and paid time off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation
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developed algorithm, pipeline, or database with application to multi-omics or image data (e.g., a Python package on PyPI or an R package on CRAN/Bioconductor). Proven experience in disease research, with a
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off. To access this tool and learn more about the total value of your benefits, please click on the following link: https://resources.uta.edu/hr/services/records/compensation-tools.php CBC Requirement
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), atomic, molecular and optical (AMO) physics or in quantum information theory. *Interested in quantum algorithm and quantum informatic research that cuts across the different subareas of physics. *Able
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simulations on the Aurora supercomputer, using AMReX (https://amrex-codes.github.io/amrex/ ) and the lattice Boltzmann method (LBM). The candidate will develop flow/geometry-aware refinement strategies that go
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dwarfs and/or the high-precision RV survey of solar-type stars being conducted with the NEID instrument on the WIYN 3.5m telescope; (2) improving technology, algorithms, and data analysis to mitigate
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
reusable libraries in Python, Scala, and/or C++, taking advantage of Spark, Hadoop, and other tool stacks as appropriate. * Developing computational and algorithmic approaches to understanding