Statistical Learning in Functional Data and 3d Imaging
This grant supports research into machine learning methods for analyzing spectra and 3D images from modern measuring devices, focusing on uncertainty analysis.
This grant is no longer accepting proposals
NRC Research Associateship Programs has archived this opportunity.
Funder: NRC Research Associateship Programs
Due Dates: February 1, 2025 | May 1, 2025 | August 1, 2025
Funding Amounts: Stipend approximately $82,764 per year plus $3,000 travel allowance; typical appointment duration 2 years.
Summary: Supports postdoctoral research developing statistical and machine learning methods for analyzing spectra and 3D imaging data from modern measurement devices, focusing on uncertainty analysis and engineering applications at NIST.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; requires contacting a NIST research adviser prior to application; NIST participates only in February and August review cycles.
Description
This fellowship opportunity at the National Institute of Standards and Technology (NIST) within the Information Technology Laboratory, Statistical Engineering Division, supports postdoctoral research focused on developing statistical methodologies and computer software to address engineering and physical measurement challenges using machine learning. The research targets data produced by modern measuring devices, such as spectra and 3D images (including hyperspectral images and Optical Coherence Tomography (OCT)).
Key research interests include:
- Statistical methodology for measurement and uncertainty analysis of high-throughput data.
- Functional data analysis in designed experimental settings.
- Both supervised and unsupervised learning techniques, including singular value decomposition for extracting spectral signatures.
- Nonparametric Bayesian methods, including empirical Bayes, for analyzing multiple parallel data sets from one or more populations.
The project aims to advance statistical learning approaches to improve engineering applications and physical measurement standards.
