Graduate Student Research Assistant- Computational Modeling of Polymer Solution Behavior

AeroVironment • Bath Township, OH

2d

About The Position

Join a multidisciplinary team at the Air Force Research Laboratory (AFRL) for a high-impact summer internship focused on the intersection of polymer physics and machine learning. As a PhD-level intern, you will contribute to the computational polymer physics research team by developing theory-informed machine learning (ML) models to predict the complex phase behavior of ternary polymer solutions. The goal of this internship is to accelerate the discovery of novel materials with tailored properties for Department of the Air Force (DAF) applications by mapping phase diagrams and kinetic pathways more efficiently than traditional simulation methods alone. Key Responsibilities - Leverage advanced ML techniques, such as theory-informed neural networks or graph neural networks (GNNs), to develop predictive models for ternary phase diagrams. - Incorporate polymer solution theory and thermodynamics (e.g., Flory-Huggins theory) into ML architectures to ensure physical consistency. - Actively engage with AFRL scientists, academic collaborators, and industry partners to refine research objectives. - Document findings and potentially contribute to a manuscript for peer-reviewed publication.

Requirements

• Currently enrolled in a Ph.D. program in Polymer Science, Chemical Engineering, Materials Science, Physics, or a closely related field.
• Strong programming skills in Python and experience with ML frameworks such as PyTorch or TensorFlow.
• Proven experience in numerical methods and computational simulations.
• Excellent oral and written communication skills.
• This position involves working within a government facility and requires US citizenship

Nice To Haves

• Deep understanding of polymer physics and thermodynamics, specifically polymer solution theory and phase transitions.
• Experience with statistical mechanics and advanced computational techniques.
• Familiarity with high-performance computing (HPC) environments.
• A record of research contributions in peer-reviewed journals

Responsibilities

• Leverage advanced ML techniques, such as theory-informed neural networks or graph neural networks (GNNs), to develop predictive models for ternary phase diagrams.
• Incorporate polymer solution theory and thermodynamics (e.g., Flory-Huggins theory) into ML architectures to ensure physical consistency.
• Actively engage with AFRL scientists, academic collaborators, and industry partners to refine research objectives.
• Document findings and potentially contribute to a manuscript for peer-reviewed publication.

Benefits

• AV offers an excellent benefits package including medical, dental vision, 401K with company matching, a 9/80 work schedule and a paid holiday shutdown.
• For more information about our company benefit offerings please visit: http://www.avinc.com/myavbenefits.

What This Job Offers

• Career Level: Intern
• Education Level: Ph.D. or professional degree