Materials Process Modeling with Machine-Learning

This research opportunity through the Army Research Laboratory Research Associateship Program (ARL-RAP) encompasses modeling of quantitative parts performance relationships utilizing state-of-the-art machine-learning (ML) technologies and tools. In traditional design, process optimization and part optimization are performed independently, ignoring the inherent dependence of materials and part properties on processing conditions. In this project, ML models will be used to extract cross-property and inverse functions in a holistic framework of the scientific design and production process. Candidates well-versed in the application and/or development of probabilistic graph models, dimensionality reduction and featurization, or neural networks for materials science or process modeling are being sought. The ARL-RAP is designed to significantly increase the involvement of creative and highly trained scientists and engineers from academia and industry in scientific and technical areas of interest and relevance to the Army. Scientists and Engineers at the DEVCOM Army Research Laboratory help shape and execute the Army's program for meeting the challenge of developing technologies that will support Army forces in meeting future operational needs.