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, 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.
The NIST Statistical Engineering Division seeks a researcher with a broad interest in statistical metrology to work on a variety of problems with NIST scientists, engineers, statisticians and other technical staff. Projects areas are likely to include research in the statistical characterization of nanomaterials to study chemical loading mechanisms in pharmaceutical applications (e.g., vaccine delivery) or assessing environmental contamination (e.g., contaminant adsorption on plastic nanoparticles), forensic science (e.g., analysis of DNA, footwear and tire tread, or other types of evidence), statistical methods for instrument calibration or the development of reference materials, characterization of semiconductor components or processes, or similar projects from a wide range of other physical science application areas. Statistical methods used may include experiment design, linear models, Bayesian modeling via Markov Chain Monte Carlo (MCMC), machine learning, or other techniques required to solve the problems at hand. Problems generally are collaborator-driven by the needs of the NIST technical staff in areas outside of statistics. Each project typically has a duration of several months to several years. Longer-term collaborative projects often have work that occurs in multiple-phases, however, and include publication of intermediate results.
Key responsibilities will include but are not limited to:
- Working with NIST scientists, engineers, statisticians, and other technical staff to understand and precisely define relevant research questions for applications of interest
- Designing experiments using principles of statistical experiment design, as needed, to answer relevant scientific research questions formulated with collaborators
- Preparing data for analysis, as needed, with an emphasis on reproducible data preprocessing pipelines for data sets requiring a significant level of preparation
- Analyzing data using both graphical methods and via statistical modeling fitting and inference using software tools and methods that support research reproducibility
- Developing software tools for analysis of data by other researchers either for specific projects or for specific computational methods (e.g., Shiny apps, R packages, etc.), as needed
- Presenting results at internal meetings and potentially to external stakeholders
- Ensuring that research results, protocols, software and documentation, or other work outputs have been shared with relevant NIST staff members or appropriately archived for future NIST use.
- An MS or PhD in statistics or a related field with a focus on modeling and inference
- Three to five years of relevant work experience
- Experience in exploratory data analysis and preprocessing data for analysis
- Experience using or developing software for statistical modeling and inference
- Ability to work both independently and in collaborative teams to solve problems
- Skill in communicating complex statistical concepts to non-statisticians
- Ability to work 30 to 40 hours/week, either individually or in total with one or more graduate student advisees who apply at the same time using the associated PhD student job posting
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.