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length scales Develop machine learning algorithms to support process optimization, predictive modeling, and intelligent manufacturing control Integrate simulation tools with in-situ sensor data from
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Requisition Id 15813 Overview: We are seeking a highly motivated postdoctoral researcher with a strong background in sensor integration, data acquisition, and in situ process monitoring
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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-sensor fusion, and propagation modeling — to develop AI-enabled detection, classification, and triangulation algorithms for critical energy infrastructure applications. This position resides within
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computational mesh generation. In this role, you will apply your software engineering skills to develop and validate computational results that support large-scale, physics-based simulations across a variety of
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package instructions for performance and documentation of repairs. Experience in the operation, calibration, and troubleshooting of radiation monitors and sensors, experience with flow instrumentation, and
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you meet the three-year residency requirement, you will be required to obtain a PIV credential to maintain employment. Postdocs: Applicants cannot have received their Ph.D. more than five years prior
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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, and real-time (GeoEvent) services. Implement and support offline GIS workflows, mobile field work, GPS-based data collection, and road network analysis (Network Analyst). Establish and maintain data
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Community (IC) mission areas. In addition to developing sensor systems across complex RF environments, you will develop prototype communication systems—implementing end-to-end transmitter/receiver chains