Location: Los Alamos, NM
Cap-Exempt H-1B Position — No lottery required
**What You Will Do** Staff in the Materials Science in Radiation and Dynamics Extremes Group (MST-8) in the Materials Science and Technology Division at Los Alamos National Laboratory (LANL) are seeking a candidate for the Graduate Research Assistant Program in computational materials science and data-driven experimental design. The position is in support of several research projects that address multi-scale problems relating to nuclear fuel systems, including oxide, metallic, and other non-oxide ceramics fuels as well as steel, zirconium, and silicon carbide cladding, as well as their performance in reactor systems. This position supports academic research focused on the development of advanced computational methods for adaptive experimental design in quantitative elemental imaging workflows. The research will involve the design, implementation, and evaluation of advanced computational algorithms that: - Identify spatial and compositional patterns in multi-element mapping datasets (e.g., gradients, segregation, inclusions, clustering, interfaces, and anomalous regions); - Recommend follow-on measurement actions to maximize information gain, including: - Identifying regions where additional raster scans should be performed, - Suggesting locations for higher-resolution or higher-statistics scans, - Proposing targeted point analyses to refine compositional estimates; - Optimize acquisition parameters (e.g., dwell time, step size, spatial resolution, and count-statistics proxies) to enhance data quality while respecting operational and instrument constraints; - Detect and manage out-of-bounds or invalid parameter selections by incorporating safe operating limits into the decision-making framework; - Integrate uncertainty quantification and data-quality metrics to prioritize measurements and avoid low-value or unreliable regions. The student will implement the developed methods in Python-based, open-source software with emphasis on reproducibility, modular architecture,