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

Active

Contract Opportunity

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, exploratory analysis, information extraction, and development or adaptation of AI, machine-learning, and LLM methods;

• AI/LLM prototyping, prompting, fine-tuning, workflow development, and comparison of alternative modeling approaches;

• Evaluation design, reference-data development, performance assessment, error analysis, and generalizability and robustness testing;

• Development and evaluation of scalable machine-learning pipelines and model-evaluation frameworks; and

• Preparation of technical summaries, analyses, methods descriptions, figures, reports, presentations, and manuscripts as required by the project.

INTENDED SOURCE

The Government intends to procure these services from the University of Maryland, College Park (UMD), Department of Electrical and Computer Engineering.

The Government's market research indicates that UMD possesses the specialized technical expertise required to support this effort. The proposed technical specialist possesses Ph.D.-level expertise in Electrical and Computer Engineering/Computer Science, with demonstrated experience in large-scale AI and foundation/language-model development and evaluation, scalable machine-learning pipelines, information extraction from unstructured text, and rigorous model-validation methodologies.

The NIH Clinical Center's Critical Care Medicine Department also has an ongoing machine-learning/AI effort with the same UMD contractor. The Government intends to leverage the iterative learning, technical knowledge, and core algorithms already developed through that effort in support of this new AI requirement. This continuity is expected to reduce duplication of effort, conserve Government resources, and facilitate timely execution of the Health and Extreme Weather study.

UMD's proximity to NIH also facilitates in-person technical collaboration and integration between the NIH Clinical Center's Clinical Epidemiology Section and UMD's AI/ML expertise.

NOTICE OF INTENT

This notice is not a request for competitive proposals or quotations. The Government intends to procure the required services from the University of Maryland, College Park.

However, all responsible sources that believe they possess the specialized technical capabilities necessary to satisfy the Government's requirement may submit a capability statement for consideration.

Interested parties must provide sufficient information demonstrating their ability to perform the complete requirement. At a minimum, capability statements should address:

1. 
   

   Company/organization name, address, Unique Entity ID (UEI), and point of contact;

   
   


2. 
   

   Business size and socioeconomic status under NAICS 541715;

   
   


3. 
   

   Demonstrated Ph.D.-level expertise in Electrical and Computer Engineering, Computer Science, or a closely related discipline;

   
   


4. 
   

   Demonstrated experience developing and evaluating large-scale AI, machine-learning, foundation-model, and/or large-language-model technologies;

   
   


5. 
   

   Demonstrated experience developing scalable machine-learning pipelines and rigorous model-evaluation frameworks;

   
   


6. 
   

   Experience applying AI/ML methods to healthcare, clinical, EMS, or other large heterogeneous datasets;

   
   


7. 
   

   Experience performing information extraction from unstructured text and evaluating model generalizability and robustness; and

   
   


8. 
   

   Sufficient information demonstrating the ability to satisfy the requirement within the required period of performance.

   
   

Capability statements must be received no later than October 8, 2026, at 6:00 AM Eastern Time (ET) and emailed to shasheshe.goolsby@nih.gov. Telephone calls are not acceptable.

Information received will be considered solely for the purpose of determining whether conducting a competitive procurement is appropriate.

A determination by the Government not to compete this proposed acquisition based upon responses to this notice is solely within the discretion of the Government.

.

Attachments/Links