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and technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will conduct comprehensive supply chain mapping, modeling, and analysis—integrating
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relationships in next-generation electronic materials. This role involves creating AI models for real-time data analysis, enabling autonomous experiments through active learning and "curiosity-driven" exploration
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design, development of supervisory control logic, validation of vehicle models against test data etc. The projects and interests of this group span all modes of transportation including off-road, rail
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-quality software. Strong software development skills, particularly in Python or C++, with experience in data analysis, simulation, or workflow scripting. Excellent verbal and written communication skills
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microscopy user program, including collaboration-building, experiment planning, and development of data processing/analysis workflows. Publish instrumentation and science outcomes in peer-reviewed journals and
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Knowledge of atmospheric dynamics, process scale models, and numerical computation techniques Knowledge of data analysis Knowledge of using atmospheric observational datasets, data assimilation techniques
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using software, such as LAMMPS, and machine-learned potentials Experience in GPU programming with Kokkos An understanding of computer architecture and experience in the analysis and improvement
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technologies, and in advancing data-driven risk monitoring approaches for supply chain resilience. The candidate will assist with data collection, analysis, and scenario modeling for a DOE-sponsored assessment
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linear, mixed-integer, and stochastic programming. Work with programming languages such as Python, Julia, or C++ to build robust analytical tools and perform large-scale data analysis. Collaborate with
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training or analysis of scaling behavior. Familiarity with challenges such as data heterogeneity, communication efficiency, or system constraints. Exposure to privacy, robustness, or security techniques (e.g