Materials Science Postdoc for 3D Imaging and Mechanics of Metallic Foam and Cellular Materials

What You Will Do The Materials Science in Radiation and Dynamic Extremes Group (MST-8) is seeking a Materials Science Postdoctoral Research Associate to conduct research at the intersection of three-dimensional imaging, mesoscale modeling, and generative artificial intelligence (AI) to investigate the dynamic response of metallic foams and cellular materials. This position is part of a newly funded project to develop adaptive, physics-constrained generative diffusion models that act as real-time digital twins of complex, time-varying systems observed through sparse measurements. For metallic foams and cellular materials subjected to dynamic compression and shock loading, a central challenge is to determine how their three-dimensional architecture including pore size, shape, and distribution; cell wall thickness, ligament connectivity; and structural defects controls deformation, collapse, damage and energy absorption. These mechanisms evolve rapidly across micrometer-scale features within millimeter-scale samples. Dynamic experiments typically provide only sparse and indirect observations such as a limited number of ultra-fast 2D detector images or 1D photon Doppler velocimetry (PDV) traces. This project will combine these measurements with mechanics-based constraints and mesoscale simulations to reconstruct the evolving three-dimensional cellular architecture and associated density, deformation, velocity, stress, and damage fields; quantify uncertainty in the reconstructed states; and predict their subsequent evolution. The resulting capabilities will support adaptive, closed-loop 4D imaging experiments and provide otherwise inaccessible insight into mechanisms such as cell-wall bending and buckling, ligament fracture, localized pore collapse, compaction-front propagation, and densification. Ultimately, the work will establish quantitative relationships among initial cellular architecture, local collapse mechanisms, and macroscopic properties such as strength, failure resistance, and energy absorption under dynamic loading. The successful candidate will: Develop and perform mesoscale continuum-mechanics and shock-physics experiments and simulations of metallic foams and cellular materials under quasi-static and high-strain-rate, and shock-compression loading. Curate, process, and analyze existing experimental datasets, including X-ray phase-contrast imaging (PCI) and PDV measurements from dynamic-compression experiments. Integrate experimental observations with simulation results to construct datasets for generative AI-enabled surrogate model development and to establish quantitative relationships among initial cellular architecture, local collapse mechanisms, and macroscopic mechanical response. Identify and formulate appropriate physical constraints for incorporation into generative models including conservation of mass, momentum, and energy. Collaborate closely with AI/ML researchers to develop, train, and validate physics-constrained latent diffusion models using simulated and experimental data. Develop validation and observation criteria to quantify reconstruction accuracy and uncertainty and determine the number, type, spatial resolution, and temporal resolution of measurements needed to recover the evolving three-dimensional cellular structure and physical state. Help design, propose, and conduct new in situ and dynamic compression experiments at Department of Energy X-ray light sources and LANL shock physics facilities. Contribute to the development and demonstration of real-time inference, adaptive measurement selection, and closed-loop experimental-feedback capabilities. The project team already has extensive expertise in AI and machine learning. The primary need for this position is therefore a strong foundation in the solid mechanics, shock physics, and experimental or computational characterization of metallic foams, cellular solids, porous materials, or related heterogeneous material systems. Of particular inter…

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