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RAP opportunity at National Institute of Standards and Technology     NIST

Uncertainty Analysis for Machine Learning and Optimization Applications


Material Measurement Laboratory, Chemical Sciences Division

opportunity location
50.64.61.C0098 Gaithersburg, MD

NIST only participates in the February and August reviews.


name email phone
David Allan Sheen 301.975.2603


Computational tools for analysis and model development have been widely used in many applications, but the predictions of these models are not complete without an estimate of the uncertainty in those predictions. With uncertainty analysis, the model predictions are made more robust and reliable. This project seeks to understand and develop techniques for uncertainty analysis in machine learning and advanced optimization for fields including precision medicine, biomanufacturing, omics research, and kinetic model development. Techniques for uncertainty estimation will range from non-parametric methods such as bootstrapping to fully Bayesian analysis.

W. F. C. Rocha, D. A. Sheen, SAR and QSAR in Environmental Research  (2016), 799-811.

D. A. Sheen, W. F. C. Rocha, K. A. Lippa, D. W. Bearden, Chemometrics and Intelligent Laboratory Systems 162 (2017), 10-20.

key words
Machine learning; Outlier detection; Optimization; Informatics; Chemometrics; Metabolomics; Bayesian analysis; Uncertainty analysis; Uncertainty quantification


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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