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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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on the design and evaluation of innovative data- and machine learning-based systems to integrate more renewable energy into our energy systems and make energy use more efficient. We develop new optimization
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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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Division, Systems Department, Directorate of Technology, Engineering and Quality. The Future Engineering Division, in support of the programme directorates, is responsible for developing and providing
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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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MesaPD to solve complex multiphysics problems. The coupling is done across package boundaries. This also requires more sophisticated approaches in load-balancing. Finally, the newly developed algorithms
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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