Sort by
Refine Your Search
-
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
-
Objectives: Under the guidance of a mentor, the follow will have the opportunity to learn the following: Extraction and sequencing of field and laboratory samples of insect vectors and microbes Analysis
-
-Resistant Organism Repository and Surveillance Network (MRSN) is a unique entity that serves as the primary surveillance organization for antibiotic-resistant bacteria across the Military Health System (MHS
-
analysis of projects relating to different aspects of vector biology, including ecology, population genetics, management strategies, virus interactions, and virus-transmission competence, depending
-
statistical software. Learning Objectives: Learn about the implementation of the application of machine learning methodologies in plant phenotyping and genotyping for the sugarcane molecular biology lab. Learn
-
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
-
agricultural land to exit dairy production thereby opening opportunities for alternative agricultural enterprises. Learning Objectives: The fellow will gain experience in planning and conducting data collection
-
findings will be encouraged and supported. Learning Objectives: The fellow will have the opportunity to gain or expand skillsets over a range of computational techniques needed for modern agricultural