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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 13 hours 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
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imbalanced cross-sectional imaging data are especially well-suited for the position . The postdoctoral researchers will develop algorithms for deployment at our prestigious R Adams Cowley Shock Trauma Center
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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
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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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linear mixed models, and biological network analysis. This may require development of new computer algorithms and/or construction of computational workflows on computer clusters. The types of multi-omics
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Center for Biomedical Informatics Research at Stanford University. This position emphasizes evaluating various cancer screening strategies by developing and applying microsimulation models for decision
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for postdoctoral researcher position to conduct collaborative research aimed at the development of novel data mining algorithm to neuroimaging, genetic, and clinical data for brain disorders such as
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. Qualifications a. Qualifications A PhD in Computer Science or Electrical Engineering with biomedical data analysis emphasis or related fields is required. Previous experience in developing novel data mining