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- Zintellect
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Field
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in the design, implementation, and maintenance of data pipelines and leading/assisting in building algorithms for deep learning with close collaboration from the study team. While programming
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will be focused primarily on the development and application of novel computational algorithms to analyze and integrate diverse omics datasets, including single-cell RNA-seq, spatial transcriptomics and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 19 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 | 19 days ago
, algorithms for dispersion, gas-phase chemistry and/or aerosol microphysics model. Preferred Qualifications, Competencies, and Experience Desired qualifications are a) atmospheric and aerosol chemistry, b
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computational models and systems using algorithms and analytics for materials and related physical sciences for a broad range of energy, transportation, and advanced manufacturing applications. Major Duties
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systems. Includes establishing medical reasoning benchmarks and automated / scalable evaluation methods. Developing recommender algorithms to predict specialty care with large-language model based user
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unravel the complex relationships between land use changes and fire regimes over the past 60 years. The successful candidate will lead efforts to: Develop advanced deep learning algorithms for classifying
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 month 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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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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quality algorithms and products (e.g., temperature, turbidity, chlorophyll-a) while developing novel methodologies to advance research on short- and long-term water quality conditions. Approaches may