Description

Overview
The Statistical Engineering Division of the Information Technology Laboratory of the National Institute of Standards and Technology (NIST) is seeking qualified candidates, U.S. citizens preferred, to collaborate with a team of world-class statisticians and metrologists 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 supporting critical and emerging technologies. The successful candidate will have the opportunity to master the application of statistical thinking and modeling to physical science problems as well as communicating with non-statistician scientists.

Duties
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
Processing large data sets in high-performance computing environments
Communicating with non-statisticians using descriptive statistics and graphical displays

Terms
This opportunity is to be an associate researcher in the NIST Statistical Engineering Division for a term of one year, with options to renew and/or pursue longer-term federal employment. Associate researchers are not federal employees, but they work alongside NIST researchers. No relocation expenses will be provided.

Requirements

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