Associate Research Scientist; PREP

General Description

PREP Research Associate

Senior Research Fellow: AI and Community Resilience

This position is part of the National Institute of Standards (NIST) Professional Research Experience (PREP) program. NIST recognizes that its research staff may wish to collaborate with researchers at academic institutions on specific projects of mutual interest, and thus requires that such institutions must be the recipient of a PREP award. The PREP program requires staff from a wide range of backgrounds to work on scientific research in many areas.

Employees in this position will perform technical work that underpins the collaboration's scientific research.

US Citizenship is required for this position.

The PREP researcher will focus time on efforts that support the project: "Cost-Effective Resource Allocation Strategies for Community Resilience" (Applied Economics Office, Dr. Jennifer Helgeson) with expert‑level consultation on the use of AI to strengthen socio‑economic analysis of community resilience alternatives.

It is expected that the PREP Researcher(s) will engage in all steps of the research process, including but not limited to: literature review, data collection, data analysis, preparation of reports and archival journal articles, as well as reporting and communication with stakeholders.

The objective of this role is to support community resilience planning through the development of methods and tools that evaluate the economic impacts of disruptive events and persistent stressors while accounting for stakeholder perceptions and associated decisions related to future event uncertainty and co‑benefit (co‑cost) valuation. The focus of this role is on the use of advanced AI techniques to augment traditional BCA efforts and to deepen understanding of socio‑economic systems through the development of LLMs and automated Mental Modeling techniques.

There will be a special focus on criteria and methods to improve the resilient performance of structural systems in the built environment while maintaining cost‑effectiveness and supporting community‑defined objectives in addition to resilience.

This particular role is focused efforts to measure, quantify and qualify large datasets of public perceptions in order to develop an analytical method that uses Artificial Intelligence (AI) reduce human analytical labor associated with summarizing public comment and perceptions of climate change and governmental proposed actions.

• Collaborate on data and methods to improve the methodology for measuring economic impact of community resilience planning, which is organized around the performance metric of "cost plus loss" minimization – i.e., the economically optimal level of investment in prevention and mitigation activities to reduce future disturbance and disaster‑related losses, as well as related expenditures that minimize the combined investment cost plus the value of expected losses.

• To develop a framework for using NLP to analyze policy documents, public comments, and social media posts at the intersection of community resilience and climate change.
• To identify key conceptions, preferences, and stakeholder‑perceived alternatives in (a) public comments and (b) social media data.
• To quantify and understand uncertainty in the use of NLP and to provide verification and validation for the developed methods.
• To develop replicable methods that complement existing community resilience economics methods for integrating, quantifying, curating, and communicating public comment features.
• To evaluate how narratives influence the development and modification of public perceptions and frame BCA questions in the realm of community resilience planning.

• Graduate degree in Computer Science, Economics, Social Science, or related field.
• At least four years of relevant research experience and ten years of teaching/advising experience.
• At least two years of proven research in the area of AI use and development of LLMs and automated processing of Mental Models.
• Background in econometrics or statistics and at least two of the following areas:
  • Applied microeconomics
  • Decision‑science or…