-
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
-
documenting insights that can inform Agency-wide AI governance and implementation efforts. You will apply machine learning methods and applied AI tools, including experience with large language models (LLMs
-
, 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
-
sensors, RGB/IR cameras, video systems, insect traps, and other devices to build predictive, AI- and machine-learning based models for monitoring grain quality and detecting deterioration due to mold
-
have an interest in substance use, posttraumatic and/or operational stress, suicidality, and artificial intelligence/machine learning (AI/ML). What will I be doing and why should I apply? As the selected
-
to identify key uncertainties in estimating direct data center water use, including development of reproducible data extraction and documentation approaches; and Develop and evaluate a regional machine learning