Sort by
Refine Your Search
-
Listed
-
Employer
- Duke University
- Princeton University
- Rutgers University
- Stanford University
- University of Washington
- The Ohio State University
- Argonne
- Nature Careers
- Oak Ridge National Laboratory
- University of Idaho
- University of North Carolina at Chapel Hill
- University of Virginia
- Cornell University
- Northeastern University
- Texas A&M AgriLife
- University of Florida
- University of Kansas
- University of South Carolina
- Yale University
- Angelo State University
- Auburn University
- Cold Spring Harbor Laboratory
- Colorado State University
- Emory University
- Indiana University
- Medical College of Wisconsin
- National Aeronautics and Space Administration (NASA)
- Purdue University
- Texas A&M University
- The University of Iowa
- U.S. Department of Energy (DOE)
- University of California Irvine
- University of California, Los Angeles
- University of California, Merced
- University of Central Florida
- University of Maine
- University of Maryland
- University of Maryland, Baltimore
- University of Miami
- University of Minnesota
- University of Nevada Las Vegas
- University of Nevada, Reno
- University of Oklahoma
- Virginia Tech
- Washington University in St. Louis
- 35 more »
- « less
-
Field
-
functions, predictive models or quantum chemistry Machine learning or AI frameworks applied to molecular discovery. Familiarity with cloud or high-performance computing environments. Experience collaborating
-
-time academic or research career. The individual will work primarily on the Duke Predictive Model of Adolescent Mental Health (Duke-PMA) study, a multi-site NIH-funded project that leverages artificial
-
None Additional Preferred Experience working in one or more of the following areas: Longitudinal data analysis Predictive modeling/machine learning models Biostatistics / epidemiological modeling
-
University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 14 hours ago
prediction using large-scale multimodal neuroimaging data. The research will emphasize methodological innovation in statistical modeling, transfer learning, machine learning, model ensemble strategies, and
-
positions: Qualifications 1. Computational Biology & Data Integration Using multi-omics microbiome analysis, single-cell transcriptomics, and machine learning–based biomarker prediction models, this project
-
of compressible flow regimes, including supersonic and hypersonic flows, as demonstrated by application materials. Familiarity with machine learning or data-driven modeling approaches in fluid dynamics, as
-
Internal Number: JR90686 Position Summary This position will focus on integrating high-resolution field monitoring, remote sensing, and statistical and numerical modeling approaches to improve predictive
-
imagery). Experience in building data models using Python or other statistical and/or mathematical programming packages. Proficiency in developing machine learning algorithms to analyze spatial-temporal
-
-based modeling of hydrological and Earth system processes. The CHAS group conducts world-class research in hydrological and Earth system modeling, large-scale data analytics and machine learning (ML), and
-
Position Summary This position will focus on integrating high-resolution field monitoring, remote sensing, and statistical and numerical modeling approaches to improve predictive flood hazard