Location: Los Alamos, NM
Cap-Exempt H-1B Position — No lottery required
**What You Will Do** The MST-8 Materials Science and Technology group at Los Alamos National Laboratory has exceptional opportunities for postdoc candidates in the field of materials modeling, particularly atomistic and mesoscale modeling of plasticity, deformation twinning, recrystallization, and damage in heterogeneous crystalline metals. The selected candidate will join the MATEE (Materials Modeling And characTerization under Extreme Environments) team within MST-8. The MATEE team focuses on the development and use of advanced computational mechanics models to predict the effects of extreme and complex environments on both mechanical response and microstructure evolution in materials. Environments considered include high stresses, temperatures, radiation, and corrosion. The MATEE team adopts both hierarchical and integrated multiscale modeling and characterization strategies for mechanical deformation problems. You should have a strong publication record, an interdisciplinary research philosophy, and the ability to effectively communicate with researchers with varied backgrounds. As a postdoctoral research associate, you will be given the opportunity to publish results in high-impact peer-reviewed journals and present at well-known conferences. The position is for two years, with a third year possible based on performance and funding. The successful candidate will lead the development and application of multiscale computational models for hexagonal close-packed materials, with emphasis on atomistic modeling, deformation twinning, twin-grain-boundary interactions, microstructure evolution, and recrystallization. The work will focus on linking atomistic and lower-scale mechanisms to mesoscale and continuum descriptions of plastic deformation and microstructure evolution, including the effects of grain boundaries, twin networks, prior microstructure, and texture. The candidate will contribute to atomistic, phase-field, and related modeling approaches to predict defo