This position is part of the National Institute of Standards and Technology (NIST) Professional Research Experience Program (PREP). NIST recognizes that its research staff may want to collaborate with researchers at academic institutions on specific projects of mutual interest and, therefore, requires those institutions to be recipients of a PREP award. The PREP program involves staff from a wide range of backgrounds conducting scientific research across various fields. Individuals in this position will perform technical work supporting the collaboration's scientific research.
Research Title:
Statistical Methods for Reference Material Value Transfer and Uncertainty Propagation
The work will entail:
This project involves collaborative research working with a team of statisticians at NIST on statistical methodology for the development of Standard Reference Materials (SRMs). The research will focus on cases where SRMs are being updated and it is desired to carry older certified values forward rather than conduct a full recertification measurement process. The project will explore the theoretical issues, risks, and statistical questions associated with this carry-forward approach. The project also involves developing theoretical methods for adjusting and propagating uncertainties when this approach is utilized.
Candidates must be eligible to obtain a Department of Commerce background check for facility access.
Key responsibilities will include but are not limited to:
- Work with scientific collaborators to help refine and clarify project goals and timelines.
- Use statistical modelling and simulation studies to explore the appropriateness of reusing certified values in a range of situations.
- Develop a theoretical framework for expanding uncertainties to account for the reuse of values.
- Implement statistical methods via a high-level programming language such as R or Python.
- Present and communicate results and statistical analysis to non-statisticians.
Qualifications
- Master’s degree in statistics or a related quantitative field.
- Experience with data analysis and statistical simulation.
- Proficiency in at least one of R or Python.
- Strong oral and written communication skills.
The university is an Equal Employment Opportunity employer that does not unlawfully discriminate in any of its programs or activities on the basis of race, color, religion, sex, national origin, age, disability, veteran status, sexual orientation, gender identity or expression, or on any other basis prohibited by applicable law.