Sort by
Refine Your Search
-
areas. These include, but are not limited to: Applying machine learning algorithms to solve real-world problems. Creating and structuring databases for storage, retrieval, and image analysis. Determining
-
phenotyping using both drone-based and ground based sensing platforms. Learn artificial intelligence and machine learning techniques to analyze image and geospatial data from diverse sources for crop monitoring
-
regions will be evaluated for features such as signatures of selection or diversifying or purifying selection, around genes and regions of agricultural importance. Learning Objectives: The participant will
-
the underlying impacts of stressors on bee health and analytical skills for understanding colony level dynamics and predicting mass colony loss events. Learning Objectives: The fellow will learn and apply
-
, software applications, record keeping, compliance training, and the principles of scientific study design. Learning both general and specialized research skills that will support advancing your scientific
-
. Through these experiences, it is anticipated that you will learn how to: Operate and develop custom aerosol generation equipment including software modifications for associated chambers. Operate
-
Raman imaging technologies for safety and quality evaluation of agricultural products. Learn artificial intelligence/machine learning methods to evaluate hyperspectral image data to assess safety and
-
, statistics, and field-lab approaches. Learning Objectives: The participant will receive training in plant molecular biology, genetics, and genomics. This research is expected to result in increased learning
-
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
-
degradation that could occur to C-130 crew members from extended exposure to environmental insults. This research will also inform the development of effective human-machine systems and healthy Airmen protocol