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 Models and Computational Algorithms for Nanoparticle Tracking Analysis
The work will entail:
The work will entail collaboration with a team of world-class statisticians and metrologists at NIST to complete the ongoing development of statistical models and computational algorithms for nanoparticle tracking analysis. The main goal of this research project is to optimize the accuracy and precision of this emerging method to measure the dimensional and optical properties of single nanoparticles with high throughput, advancing fundamental metrology of nanoparticle standards and samples, and thus supporting critical and emerging technologies of biotechnology and semiconductors. The project team is in the midst of developing a Bayesian framework for inference from multiple sources of input data: optical micrographs of nanoparticle diffusion in microfluidic confinement; optical microscope calibrations; thermometry and viscometry results, and molecular dynamics simulations. In support of this main goal, the project also involves the evaluation of atomic force micrographs of standards for microscope calibration. Completion of the framework requires expertise in statistical modeling, in particular Bayesian inference, image processing, diffusion analysis, molecular dynamics simulations, and communicating with non-statisticians.
Candidates must be eligible to obtain a Department of Commerce background check for facility access
Key responsibilities will include but are not limited to:
- Creating statistical models, primarily a Bayesian framework, and computational algorithms in support of metrology goals
- Processing micrographs and reducing data in support of statistical modeling, including the creation of custom computational algorithms for processing as necessary
- Performing molecular dynamics simulations in support of statistical modeling
- Creating computer-model surrogates in support of molecular dynamics simulations
- Processing large data sets in high-performance computing environments
- Exploring data by descriptive statistics and graphical displays
- Explaining statistical concepts to non-statisticians
- Translating physical concepts from non-statisticians into statistical concepts
Qualifications
- A Ph.D. in statistics or a related area of academic study
- Experience with nanoparticle tracking analysis
- Experience processing and localizing sub-resolution structures in optical and atomic-force micrographs
- Experience with optical and atomic-force microscope calibration
- Experience with statistical, including Bayesian, modeling
- Experience with optimization, Monte Carlo, and Markov Chain Monte Carlo algorithms
- Experience with Large-scale Atomic/Molecular Massively Parallel Simulator (LAMMPS)
- Experience creating Gaussian process computer-model surrogates
- Experience with high-performance computing
- Strong communication skills in speaking, writing, and graphical display
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.