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clinical pulmonologists and immunologists to study the molecular mechanisms that underly airway tissue homeostasis and asthma pathogenesis. In addition, our group aims to develop new computational algorithms
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Systems will participate in the research efforts of developing systems integration, analysis, design, control, and/or optimization models and algorithms for smart energy systems to enable smart and healthy
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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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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 15 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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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 | 15 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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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
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. This includes integrating LLMs with structured data sources to develop robust computational phenotyping algorithms and scalable models for real-world evidence generation. The role will involve both method