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research user workflows. Preferred: experience with geospatial data workflows, including large geospatial/raster/vector datasets, spatial ETL pipelines, and performance considerations for geospatial
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computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads. Optimize resource allocation for simultaneous testing tasks and real-time tracking
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computing solutions to efficiently scale testing environments supporting large datasets and high-performance AI workloads. Optimize resource allocation for simultaneous testing tasks and real-time tracking
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) Fixed-effects panel models Matching methods (PSM, CEM, nearest-neighbor) Regression-based normalization Longitudinal and cross-sectional data analysis. Experience working with large administrative
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methodologies as well as subsized mechanical testing methodologies on highly irradiated materials (either using ions or research reactor irradiation data) for establishing performance envelopes for materials
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clustered computing software solutions to researchers who process large data sets, run simulations, and develop scientific code as part of their projects. Design, develop, optimize, and maintain scientific
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consequence management for DOE, DOD, and DHS sponsors. In this role, you will serve as a senior technical leader responsible for the architecture, development, and sustained operation of enterprise AI and data
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complex technical problems. Experience programming FPGAs, PLCs, using LabView, and operational experience with large accelerator facilities. Knowledge of Failure Mode Effects Analysis (FMEA) and
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photonic quantum sensing and computing. Experience with control electronics, data acquisition systems, machine learning and AI for control and optimization of experimental apparatus. Experience with vacuum
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, and other needs for DOE, DOW, and DHS sponsors. In this role, you will design, develop, and operate enterprise AI and data infrastructure, helping to build, maintain, and scale Docker-based