NIST Data-driven Model Development for Property Prediction in Alloys and Composites
NIST is developing data-driven models using machine learning and simulations to predict properties of alloys and composites for applications like energy and nanotechnology.
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 (closed) | August 1, 2025
Funding Amounts: $82,764 stipend plus $3,000 travel allowance; typical appointment duration is 2 years.
Summary: Postdoctoral fellowship supporting data-driven model development using machine learning and simulations to predict properties of alloys and composites at NIST for applications in energy, nanotechnology, and sensing.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; applications require prior contact with a NIST research adviser; NIST participates in February and August review cycles only.
Description
This fellowship opportunity at the National Institute of Standards and Technology (NIST) supports postdoctoral research focused on developing data-driven predictive models for the properties of alloys and composites. The program aims to accelerate advanced materials discovery by integrating cheminformatics, machine learning, and state-of-the-art computational materials science methods. Research efforts include building a comprehensive database of thermophysical and transport properties, and applying rigorous top-down theoretical and simulation approaches such as atomistic and density functional theory simulations.
Key research areas include:
- Determination of structural, thermophysical, and transport properties of metal alloys, carbon-based composites, and solid-state biomolecule hybrid structures.
- Development of predictive empirical models leveraging large experimental datasets.
- Investigation of thermal transport and interfacial phenomena relevant to nanotechnology, energy, and sensing applications.
- Improvement of atomistic simulation methodologies through system-specific refinement of interatomic interactions.
This program is part of broader initiatives like the Materials Genome Initiative, aiming to reduce the time and cost of materials development.
