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
-
. Under the direction of cognizant Federal personnel, participating researchers have opportunities to learn how to lead tasks, explore new research areas for DHS, and participate in networking opportunities
-
are not limited to, Developing new computational methods and analytical tools, with particular emphasis on machine learning and artificial intelligence approaches. Identifying signatures of viral adaptation
-
for wheat, barley, oat, and rye. As part of a highly collaborative, multi-disciplinary team, the selected candidate will use his/her computational biology and machine learning background to help develop tools
-
about the most recent advances in machine learning and data management in agricultural research. The participant will have the opportunity to collaborate with multiple USDA ARS scientists on using machine
-
and often different from the canonical types of data used to benchmark machine learning (ML) algorithms. In this opportunity, we will be evaluating how state-of-the-art ML techniques can be used
-
to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including artificial intelligence (AI) and machine learning, to help
-
also explore data integration techniques, apply machine learning approaches, and utilize high-performance computing resources to contribute to achieving the project objectives. Learning Objectives: With
-
the research and critical thinking is expected for this opportunity. Learning Objectives: The participant will engage in a comprehensive, hands-on learning experience designed to build expertise in laboratory
-
selection. This research is conducted in a collaborative environment within the lab and larger unit, so the participant will learn how to perform both independently in the field and as part of larger teams