Sort by
Refine Your Search
-
improving plant health using machine learning and artificial intelligence. Mentor(s): The mentor for this opportunity is Yulin Jia (yulin.jia@usda.gov ). If you have questions about the nature of the research
-
and Data Science (including machine learning and AI for defense applications) - Systems Engineering and Engineering Management - Industrial Engineering and Production Management - Mathematical Modeling
-
well as preliminary research on yield prediction modeling. Learning Objectives: The participant will develop skills in agricultural predictive yield modeling. These will include analysis and interpretation of large UAV
-
-Docs, post-Bacs, summer internships, etc.) to those interested in research in the following fields: Theory and application of machine learning and artificial intelligence including Natural
-
on the blockchain. A hands-on familiarity with machine learning and blockchain or related research is required as are Python or other coding skills. Quantum computing – This research is exploratory, applying hands
-
-property relationships, statistics and probability, applied mathematics, data science, or machine learning. Application Requirements A complete application consists of: Zintellect Profile Educational and
-
computing facilities at the DOD Supercomputing Research Center. What will I be doing? This project will focus on learning, adapting, and applying US Army Corps of Engineers-developed or supported coastal
-
. Description The Office of Global Research (OGR), at the National Institute of Allergy and Infectious Disease (NIAID), National Institutes of Health (NIH), is seeking candidates who are interested in learning
-
of laboratory mentors. Activities will include computer programming related to database development, extension of the IDS graphical user interface, and integration of our crop and soil models. Database activities
-
statistical analyses using a range of software tools. Learning Objectives: They will learn how plants and soils respond to genetic, environmental, and management inputs, and why understanding these interactions