Sort by
Refine Your Search
-
, and environmental factors associated with diabetes incidence, prevalence, complications, and trends. Learn and apply advanced statistical and machine learning methods, including cluster analysis and
-
, geosciences, biology (non-medical), mathematics, engineering, computer or computational sciences, or specific areas of environmental sciences that are aligned with the mission of the Office of Science are
-
interacts with the human body—making it a key factor in both product performance and consumer satisfaction. You will also apply statistical and machine-learning tools to explore how cotton fiber properties
-
) framework to assess biological risks associated with blast overpressure (BOP) from military weapon systems. You will engage in research and applied computational activities to model blast-induced energy
-
, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning (ML), to help solve complex agricultural
-
Citizenship: U.S. Citizen Only Degree: Master's Degree or Doctoral Degree received within the last 60 months or currently pursuing. Discipline(s): Computer, Information, and Data Sciences (17 ) Engineering (1
-
of tropical fruit, vegetable and ornamental crops grown in the Pacific Basin.. During this fellowship you will engage with research to extend existing computer models of surveillance traps for invasive insects
-
and research in several areas. These include, but are not limited to: Adversarial location and network interdiction models Adversarial machine learning attacks and defense (e.g., against Bayesian
-
artificial intelligence and machine learning, will be woven through each of these three areas. Benefits to you as a SMaRT Intern: The interdisciplinary atmosphere provided at UT-ORII will expose you to team
-
to identify key uncertainties in estimating direct data center water use, including development of reproducible data extraction and documentation approaches; and Develop and evaluate a regional machine learning