Sort by
Refine Your Search
-
components to build scalable digital twins. • Generative AI: Application of Large Language Models (LLMs) to generate synthetic data for digital twins and their role in supporting cyber-physical systems
-
, including the Far-Infrared Probe concept, PRIMA. We particularly encourage applicants with interests in processing and analysis of large data sets, development of scientific software, data pipelines, applied
-
of large language models (LLMs). Skill development in scientific and technical writing, data engineering, machine learning, and data visualization. Exposure to cross-agency collaborations and the opportunity
-
application consists of: An application Transcripts – Click here for detailed information about acceptable transcripts A current resume/CV, including academic history, employment history, relevant experiences
-
, including topic modeling, named entity recognition, and text-data analysis. Learn to apply large language models to real-world environmental health questions and public health challenges. Participate in
-
institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences, and
-
methods to clean large line-level datasets, append and validate geospatial data, and create impactful visualizations, based on program needs and resources Describe knowledge gaps between theoretical and
-
complete application consists of: An application Transcripts – Click here for detailed information about acceptable transcripts A current resume/CV, including academic history, employment history, relevant
-
complete application consists of: An application Transcripts – Click here for detailed information about acceptable transcripts A current resume/CV, including academic history, employment history, relevant
-
internal institution systems may be submitted. Click here for detailed information about acceptable transcripts. A current resume/CV, including academic history, employment history, relevant experiences