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RAP opportunity at National Institute of Standards and Technology     NIST

Statistical Learning in Functional Data and 3d Imaging


Information Technology Laboratory, Statistical Engineering Division

opportunity location
50.77.61.C0230 Gaithersburg, MD

NIST only participates in the February and August reviews.


name email phone
ZQ John Lu 301.975.3208


This project provides research opportunities  to develop statistical methodology or computer software to address increasing needs at NIST on using machine learning to solve interesting engineering applications or physical measurement problems. Modern measuring devices often produce data in the form of spectra or 3d images (such as hyperspectral images and OCT),  the goal is to provide statistical methodology for measurements and uncertainty analysis based on such high throughput data. Functional data analysis in designed experiments settings is often encountered in standard developments. Both supervised learning and unsupervised learning including  singular value decomposition to extract spectral signatures are important  and are our current interests.  We're also interested in using nonaparmetric Bayes, including empirical Bayes methods for analyzing parallel and many data sets sampled from one or multiple populations.  


key words
Data science; sensors; image diagnosis; classification and prediction.


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral applicants


Base Stipend Travel Allotment Supplementation
$82,764.00 $3,000.00
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