220 machine-learning "https:" "https:" "https:" "https:" "U.S" research jobs in United States
Sort by
Refine Your Search
-
Listed
-
Program
-
Employer
- Loyola University
- Brookhaven National Laboratory
- University of Washington
- Oak Ridge National Laboratory
- The University of Chicago
- University of Arkansas
- U.S. Department of Energy (DOE)
- National Aeronautics and Space Administration (NASA)
- Zintellect
- Cornell University
- University of California Los Angeles
- University of Nevada, Reno
- University of Texas at Arlington
- University of Alabama, Tuscaloosa
- Georgia Southern University
- Stanford University
- University of California Irvine Health
- University of Florida
- University of Nevada Las Vegas
- University of North Carolina at Chapel Hill
- Vanderbilt University
- Georgetown University
- National Renewable Energy Laboratory NREL
- New York University
- Oregon State University
- Princeton University
- SUNY Polytechnic Institute
- Syracuse University
- University of California Riverside
- University of California, Merced
- University of Michigan
- University of Minnesota
- University of Pennsylvania
- University of St. Thomas
- University of Texas at El Paso
- University of Utah
- AbbVie
- Argonne
- Auburn University
- Bucknell University
- Central Michigan University
- Columbia University
- Iowa State University
- Johns Hopkins University
- Missouri University of Science and Technology
- Montana State University
- Pennsylvania State University
- Rice University
- SUNY University at Buffalo
- Texas A&M University
- Texas Christian University
- The University of Memphis
- University of California
- University of California Davis
- University of California, Los Angeles
- University of Colorado
- University of Nebraska–Lincoln
- University of Virginia
- Washington State University
- 49 more »
- « less
-
Field
-
, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human
-
National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 17 hours ago
that are facile with computationally efficient, rigorous machine learning for image region identification, demonstrate an understanding of both planetary and scalable computer science, and have publication
-
National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 17 hours ago
of radiance data from new hyperspectral infrared instruments such as IASI-NG, MTG-IRS Enhancement of CrIS radiance assimilation algorithm are highly encouraged. - Use machine learning methods to cope with model
-
Pest Management in The Western U.S.' (https://ai4sa.ucr.edu/ ). The overall goal of this project is to develop advanced tools for early stress (abiotic and biotic) detection and decision support for crop
-
Organization U.S. Department of Energy (DOE) Reference Code DOE-Scholars-2026-ARPA-E How to Apply Click on Apply below to start your application. Application Deadline 2/9/2026 8:00:00 AM Eastern
-
provide students a transformative, globally connected learning experience. Consistently ranked among the nation’s top universities by U.S. News & World Report, Loyola is a STARS Gold-rated institution
-
online databases or interactive websites. Learning Objectives: TUnder the guidance of a mentor, the participant will learn techniques in genomic epidemiology and machine learning to quantify drivers of IAV
-
Department Booth Faculty Research - Research Professional About the Department The University of Chicago Booth School of Business is the second-oldest business school in the U.S. and second to none
-
connected learning experience. Consistently ranked among the nation’s top universities by U.S. News & World Report, Loyola is a STARS Gold-rated institution that is ranked as one of the country’s most
-
approaches for using machine learning to analyze X-ray data, particularly Resonant Inelastic X-ray Scattering (RIXS). The position will collaborate with experts in RIXS experiments (Mark Dean), computational