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, pyrometry, spectroscopy, co-axial and off-axis high speed imaging, and more) for process monitoring and diagnostics. Develop and implement data acquisition, signal processing, and data analytics frameworks
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to contribute to development of alloys with desirable advances in mechanical properties, thermal/electrical properties, and processability. A background in solidification processing, high pressure die casting
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compliance, reproducibility, and interoperability across scientific domains. By improving data readiness processes, this role will amplify the potential of AI-driven discovery in areas such as high energy
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to develop AI-enabled, low-latency signal-processing algorithms for next-generation pixel detectors used in high-energy physics experiments. This position offers the opportunity to engage in cutting-edge
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, priorities, and interactions with our core values of Impact, Integrity, Teamwork, Safety, and Service. Promote equal opportunity by fostering a respectful workplace – in how we treat one another, work together
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through papers, artifacts, and presentations at top-tier venues. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, a physical/computational science discipline (e.g., physics, chemistry
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and machine-learning-driven optimization frameworks for polymer composite manufacturing processes. This position resides in the Composites Innovation Group in the Manufacturing Science Division (MSD
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, finite volume, and machine learning to solve challenging real-world problems related to structural materials and advanced manufacturing processes. The successful candidate will have experience with
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in the areas of Hydrological and Earth System Modeling and Artificial Intelligence (AI). The successful candidate will have a strong background in computational science, data analysis, and process
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on designing system software for automating processes such as intelligent data ingestion, preservation of data/metadata relationships, and distributed optimization of machine learning workflows. Collaborating