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to seek an optimal integration between the physical representations of the various processes and the computing power of the AI algorithms. Key duties Develop a robust framework to simulate streamflow
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that amplify human potential. The successful candidate will engage in innovative research projects in ML, focusing on developing novel ML algorithms, enhancing human-AI collaboration, and exploring systems
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
of models and observational data using Python, Matlab, R, etc. Expertise required in developing new models, algorithms for dispersion, gas-phase chemistry and/or aerosol microphysics model Preferred
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
) strong communication skills – written and oral, c) ability to develop/translate model algorithms and develop new model code in Fortran, d) software skills needed to work with multiple observed and model
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position to develop and apply advanced analysis methods, including artificial intelligence and machine learning algorithms and approaches, for x-ray science and instruments. These methods will accelerate
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scale and resolution. This ambitious project spans multiple institutes including the Wu Tsai Neurosciences Institute, Stanford Bio-X, and the Human-Centered Artificial Intelligence Institute, bringing
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a 3-year DOE-sponsored project that started in September 2024. The Postdoctoral Research Associate working on P1 will develop and test deep learning algorithms for model emulation and model parameter
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
looking for a postdoctoral fellow interested in developing either machine learning algorithms for high-resolution histopathology imaging/spatial-profiling data in combination with other modalities (e.g
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this interdisciplinary project, we are looking for a strong candidate to contribute to the development of quantum algorithms and applications, focusing on quantum walks and quantum machine learning on graph structures
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the different types of systems and develop a core graph data system that can serve as a common building block. This way, redundancies in keeping multiple cop-ies of graph data in different systems could be