This position is part of the National Institute of Standards and Technology (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, 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 scientific research of the collaboration.
Research Title: Statistical and Machine Learning Methods to Advance Science and Technology Through Visualization, Estimation, Uncertainty Quantification, and Predictive Algorithms
The work will entail:
The work will entail collaborative statistical-analysis and machine-learning research with NIST scientists, engineers, and statisticians in support of applied metrology for physical science or biological applications of mutual interest to the Associate and to NIST. Programmatic areas of research at NIST include chemistry, materials science, forensics, semiconductor technology, physics, civil engineering, biology, and fire science among a wide range of other application areas. As an example, one potential project involves the development of methods to measure the dimensional and optical properties of single nanoparticles with high throughput by tracking their diffusion in a liquid with an optical microscope. Nanoparticles of particular interest include nanoplastic standards and nanomedicine analytes. The methods should provide not only an accurate measurement of size but also an accurate estimate of sizing uncertainty so that both quantities are available for downstream analyses such as correlating size with intensity, for each particle from the same time-series of optical micrographs. The project would impact standards development, nanomedicine characterization, and nanoplastic assessment.
Key responsibilities will include but are not limited to:
- Exploration of data including descriptive statistics and graphical displays
- Statistical modeling or machine learning in support of collaborative research goals
- Image processing in support of statistical modeling and machine learning
- High-performance computational techniques for processing large data sets
- Explaining complex statistical concepts to non-statisticians
Qualifications
- Researcher with a Ph.D. in statistics or a related area of academic study
- Experience with statistical and machine learning models and computing (e.g., R and Python)
Strong oral and written communication skills
The university is an Equal Employment Opportunity/Affirmative Action 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.