Theory and Computer Simulation of Soft Materials Using Machine Learning
Using machine learning with theory & simulation to study phase changes, self-assembly, and aggregation in polymers, colloids, and biological materials.
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 | August 1, 2025
Funding Amounts: Typical stipend approximately $82,764 for 2-year term appointments.
Summary: Supports postdoctoral research using machine learning combined with theory and computer simulation to study phase equilibrium, self-assembly, and aggregation in soft materials such as polymers, colloids, and biological materials.
Key Information: Open to U.S. citizens with a doctoral degree earned within the last 5 years; research conducted at NIST; requires contacting a Research Adviser prior to application.
Description
This fellowship opportunity under the NRC Research Associateship Programs supports advanced research at the National Institute of Standards and Technology (NIST) focused on the theory and computer simulation of soft materials using machine learning techniques. The research aims to investigate a broad range of phenomena in soft matter, including phase equilibrium, self-assembly, and aggregation processes in biological materials, polymers, colloids, and other complex fluids.
The project integrates statistical mechanical theory, computer simulations, and machine learning approaches. Key research activities include developing novel sampling methods, coarse-grained multi-scale modeling, and training machine learning models on simulation and experimental data to accelerate understanding and prediction of soft material behaviors.
This program offers a prestigious postdoctoral fellowship with access to state-of-the-art facilities and mentorship from leading scientists at NIST, providing a platform for early-career researchers to advance their expertise in computational chemistry, statistical mechanics, molecular simulation, and machine learning applied to material science.
