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Field
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. The Kommagani Laboratory was among the first to establish a causal role for the microbiome in endometriosis and continues to lead innovative efforts to develop microbiota-based diagnostics and therapeutics
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-driven mathematical modeling in a transdiagnostic clinical cohort. https://impact-mh.org/awardees/impact-mh/ IMPACT-Y is collecting repeated measures from approximately 2,400 individuals across multiple
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: Develop physics-based, data-driven, and hybrid (physics-informed ML) models of thermal systems to capture dynamic thermal behavior Validate models against experimental data and refine model accuracy and
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combining artificial intelligence with human expertise. The lab develops knowledge-grounded artificial intelligence, text mining and knowledge assembly, mathematical and systems modeling, and causal analysis
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. Sambandh Dhal's SAGE Lab (Sensing, AI & Green Systems Engineering) in the Department of Bioproducts and Biosystems Engineering at the University of Minnesota. The SAGE Lab develops trustworthy, data-driven
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data and develop data-driven methods for predicting land loss and ecosystem transitions in wetland-rich landscapes of the Gulf Coast with a focus on coastal Louisiana. This candidate will directly
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scales from microchannels to full rack systems (5.) Develop physics and AI-based models to elucidate physics, perform optimization, and create surrogate models for system level simulators (6.) Participate
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preclinical models and retrospective human MTLE datasets. Apply complex signal-processing approaches to analyze animal and human intracranial EEG. Develop and implement computational network analyses
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About the Opportunity Postdoctoral Associate – Gene Therapy and Transport Modeling Bajpayee Lab at Northeastern is seeking an exceptional Postdoctoral Associate to lead high-impact research in
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 17 days ago
inform representations and encoder adaptations. This activity will provide hands-on experience in making three-dimensional vegetation structure accessible within data-driven geospatial modeling processes