Exploring Material Behavior Across Scales: Mechanical Characterization, Microstructural Analysis, FEA/AI/ML Modeling, and Automation Approaches
Researchers will use advanced techniques like AI to study how material structure affects mechanical properties across different scales and conditions.
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; typical appointment duration 2 years.
Summary: Postdoctoral fellowship to develop and apply advanced mechanical characterization, microstructural analysis, and FEA/AI/ML modeling techniques to study material behavior across scales, stress states, and temperatures.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; research conducted onsite at NIST in Gaithersburg, MD; requires prior contact with Research Adviser.
Description
This postdoctoral fellowship opportunity at the National Institute of Standards and Technology (NIST) focuses on advancing the understanding of processing-structure-property-performance (PSPP) relationships in materials. Researchers will employ and develop cutting-edge measurement and automation techniques, including mechanical characterization, microstructural analysis, and computational modeling using finite element analysis (FEA), artificial intelligence (AI), and machine learning (ML).
The research aims to investigate material behavior across multiple length scales, stress states, and temperature conditions. Materials of interest include additively manufactured materials (such as powder bed fusion, direct energy deposition, and additive friction stir deposition) and newly developed wrought materials relevant to automotive and aerospace industries.
Mechanical characterization methods include quasi-static and high-rate servo-hydraulic load frames, compression and tension split Hopkinson bars (Kolsky bars), and instrumented hardness testing (Vickers and nanohardness). Real-time surface deformation will be captured using Digital Image Correlation (DIC) and thermography.
Microstructural analysis will utilize advanced techniques such as scanning electron microscopy (SEM), electron backscatter diffraction (EBSD), energy dispersive spectroscopy (EDS), and synchrotron-based X-ray diffraction (XRD). Thermophysical properties like heat capacity and thermal conductivity under nonequilibrium conditions will also be measured.
FEA and AI/ML modeling will be applied to discover new correlations between processing conditions, microstructure, and mechanical performance, enabling predictive models that enhance understanding of material behavior.
The program encourages applicants with diverse expertise in automation, mechanical testing, microstructural characterization, modeling, and data science.
