AI and Data Science Specialist Support – Health and Extreme Weather Project

Title: AI and Data Science Specialist Support – Health and Extreme Weather Project Agency: Department of Health and Human Services (HHS) Sub-Agency: National Institutes of Health (NIH), Clinical Center (CC) Department: Critical Care Medicine Department (CCMD), Clinical Epidemiology Section NAICS Code: 541715 – Research and Development in the Physical, Engineering, and Life Sciences (except Nanotechnology and Biotechnology) PSC: R425 – Support–Professional: Engineering/Technical Intended Source: University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering Place of Performance: University of Maryland, College Park, Maryland Period of Performance: Period 1: October 15, 2026 – October 14, 2027 Period 2: October 15, 2027 – October 14, 2028 Response Deadline: October 8, 2026, at 6:00AM Eastern Time (ET) DESCRIPTION The National Institutes of Health (NIH), Clinical Center (CC), Critical Care Medicine Department (CCMD), Clinical Epidemiology Section intends to procure specialized Artificial Intelligence (AI) and Data Science Specialist support for the NIH Health and Extreme Weather Intramural study. The Health and Extreme Weather study is a two-year project examining whether emergency department and hospital overcrowding worsens during extreme heat and how these conditions affect mortality. The project also seeks to apply artificial intelligence, including large language models (LLMs), to Emergency Medical Services (EMS) free-text narratives to identify patients with heat exposure that may not be captured through structured coding. The requirement involves specialized technical support in artificial intelligence, machine learning, large language models, data science, and analysis of large and heterogeneous healthcare datasets. Required support may include, but is not limited to: • Technical consultation and study support related to research questions, data feasibility, analytical approaches, and study/evaluation design; • Data preparation, e