Sort by
Refine Your Search
-
Category
-
Program
-
Field
-
into scalable, high-performance code. Participants will have the opportunity to learn to apply and hone these skills and acquire additional ones as they work on real-world problems. Examples of Research Areas
-
techniques. You will have the opportunity to participate in various projects utilizing artificial intelligence (AI) and machine learning (ML) to develop applications that optimize combat casualty care
-
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
-
(USAMRDC MRIID). What will I be doing? As an Oak Ridge Institute for Science and Education (ORISE) participant, you will engage in a collaborative learning experience alongside a multidisciplinary community
-
. Learning Objectives: You will learn many innovative techniques (electrospinning and microencapsulation) related to the chemical modification of cotton and other natural fibers, nanoparticle synthesis and
-
USDA-ARS Postdoctoral Research Opportunity: Development of Novel Vaccines for Poultry Viral Diseases
of the agency is to provide global leadership in agricultural discoveries through scientific excellence. Research Project: You will be part of the research team learning about developing recombinant Marek's
-
techniques like liquid chromatography mass spectrometry. Learning Objectives: Under the guidance of a mentor, you will: Learn best practices for protecting pollinators from unintended insecticide exposure
-
, data engineering, data analytics, artificial intelligence, machine learning, deep learning, natural language processing, and automation using modern tools and techniques. During this fellowship, you will
-
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 problems that also depend
-
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 problems that also depend