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Opportunity at Air Force Research Laboratory (AFRL)

Towards Developing Next-Generation Multi-Functional Composites for Aerospace Applications using Multi-scale Modeling and Machine Learning Approaches


Materials & Manufacturing, RX/Structural Materials Division

RO# Location
13.25.05.C0248 Wright-Patterson AFB, OH 454337817


name email phone
Varshney, Vikas 937.255.2568


There is an ever-changing, constant need for designing novel composite materials for aerospace applications that offer a broader gamut of multi-functionality (sensing, electrical and thermal properties, desired interfaces characteristics, adhesion, energy storage/harvesting, low density, etc.) and structural stability (mechanical behavior, stiffness or compliance, fracture toughness, high temperature stability, minimal physical aging, etc.) with an eventual goal of minimizing costs and maximizing operational performance and efficiency. Experimentally, this design space is often explored via building upon previous reported literature towards synthesizing and characterizing state-of-the-art composite materials for various applications. This opportunity seeks motivated candidates to employ well-formalized modeling methods (quantum chemistry, molecular dynamics, coarse-grain simulations) as well as to develop new data-analytics/machine-learning/AI frameworks to investigate and understand structure-property-performance relationships in multi-functional polymeric matrix composites (PMCs) and ceramic matrix composites (CMCs) to a) complement experimental efforts towards better appreciation of molecular origins of structure-property-performance relationships; and b) facilitate accelerated materials’ design for next-generation multi-functional composites, geared towards aerospace applications. The researcher will work along with a number of AFRL (Air Force Research Laboratory) scientists and engineers towards solving complex problems associated with how molecular chemical structure influences the macroscopic properties of composite materials as well as apply data analytics tools to enhance the accuracy of computational predictions for different properties of interest.

1. Radue, M.S.; Varshney, V.; Baur, J. W.; Roy, A. K.; Odegard, G. M. Molecular Modeling of Cross-Linked Polymers with Complex Cure Pathways: A Case Study of Bismaleimide Resins. Macromolecules, 2018, 51 (5), pp. 1830–1840.

2. Varshney, V.; Unnikrishnan, V.; Lee, J.; Roy, A. K.; Developing nanotube junctions with arbitrary specifications. Nanoscale, 2018, 10, pp 403–415.

3. Varshney, V.*; Roy, A. K.; Baur, J. W.; Modeling the role of bulk and surface characteristics of carbon-fiber on thermal conductance across the carbon-fiber/matrix Interface. ACS Applied Materials & Interfaces. 2015, 7 (48), pp 26674–26683.

Multi-Scale Modeling; Machine Learning; Polymer Matrix Composites (PMCs); Ceramic Matrix Composites (CMCs)l Structure-Property Relationships; Nanocomposites; Molecular Dynamics; Quantum Chemistry; Nanostrutures; 2D Materials


Citizenship:  Open to U.S. citizens
Level:  Open to Postdoctoral and Senior applicants


Base Stipend Travel Allotment Supplementation
$76,542.00 $4,000.00

$3,000 Supplement for Doctorates in Engineering & Computer Science

Experience Supplement:
Postdoctoral and Senior Associates will receive an appropriately higher stipend based on the number of years of experience past their PhD.

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