Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
Wyndmoor, PA investigates issues related to the utilization of milk, and dairy manufacturing by-products for the development of novel food, bioactive and packaging ingredients. The Unit is seeking a
-
the opportunity to develop knowledge and experience in: Engineering principles governing agricultural application systems. Atomization, droplet transport, and deposition processes. Experimental methods
-
communications. Gain experience developing communication items for community members, including newsletters, announcements, and other related content. Explore available collaboration platforms to gain experience
-
laboratory workflows, aligning scientific outputs with CDC’s commitment to excellence. Research project: We are seeking a full-time ORISE fellow to train with the Quality team (50%) and the Insecticide
-
communication preferences associated with vaccines, immunization, and respiratory illness prevention. Participants may also gain exposure to communication strategies used to educate diverse audiences about
-
on funding availability, project assignment, program rules, and availability of the participant. What are the provisions? You will receive a stipend to be determined by AFIT. Stipends are typically based on a
-
resistance to diseases and pests (nematodes, insects, and weeds) with favored quality characters and improved yield potentials; and b) to develop knowledge on disease and pest biology, ecology, and
-
: This fellowship provides an immersive research and professional development experience for a postdoctoral scholar interested in postharvest fruit quality, analytical chemistry, and metabolomics. You will
-
program that includes chemists, geneticists, and plant physiologists. Over the course of this opportunity, you will develop a thorough understanding of cotton breeding, oilseed engineering, genome editing
-
. Additionally, we intend to measure root water uptake using sap flow meters. The data will be integrated using recently developed physics-informed neural networks in order to translate apparent resistivity data