NIST Machine Learning for High Throughput Materials Discovery and Optimization Applications
NIST is funding machine learning algorithms to analyze materials data, especially hyperspectral data from combinatorial libraries, to accelerate materials discovery.
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 | November 1, 2025
Funding Amounts: Stipend approximately $82,764 per year plus $3,000 travel allowance; 2-year term appointments at NIST.
Summary: Supports postdoctoral research developing machine learning algorithms for high throughput analysis of materials data to accelerate discovery and optimization of advanced materials at NIST.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; applications require contacting a NIST Research Adviser prior to applying.
Description
This fellowship opportunity at the National Institute of Standards and Technology (NIST) supports postdoctoral researchers in developing advanced machine learning algorithms aimed at accelerating the discovery and optimization of advanced materials. The work is part of the Materials Genome Initiative (MGI) and focuses on integrating machine learning with solid state physics, experimental and theory-based materials databases, and measurement instrument theory (e.g., X-ray diffractometers).
The primary research interest is in high throughput analysis of experimental data collected from combinatorial "libraries" of materials, both offline and in real-time during experiments, to provide live feedback and guidance to experimentalists. A particular emphasis is placed on hyperspectral methods for analyzing spectra collected over two-dimensional coordinates, such as X-ray diffraction and Raman spectra.
Machine learning techniques of interest include constraint programming, Bayesian methods, sparse kernel machines, graphical models, and latent variable analysis. Materials classes relevant to this research include photovoltaic, thermoelectric, thermochromic, multiferroic, magnetic, and superconductive materials.
