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setting. Familiarity with longitudinal methods (e.g., mixed-effects models, generalized estimating equations [GEE], interrupted time series) and ability to adapt methods to diverse healthcare metric
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enrichment. A successful candidate will collaborate with computer scientists and experimental facilities to develop verified models focused on enrichment technologies. This position resides in the Gas Testing
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-scale, heterogeneous datasets to develop and deploy AI-driven methods for: Real-time quality monitoring and control of manufacturing processes Understanding relationships between manufacturing intent
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Requisition Id 16254 Overview: Do you have a passion for applying AI methods for accelerating scientific discoveries and an ability to think outside of the box in a collaborative and open
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, generalized estimating equations [GEE], interrupted time series) and ability to adapt methods to diverse healthcare metric analysis. Proficiency in survey design and statistical analysis. Experience working in
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Requisition Id 16263 Overview: We are seeking a group leader who will focus on AI/ML methods/application, image/signal processing, and sensor systems. This position resides in the Multimodal Sensor
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application of modern analysis tools and methods to upgrade the HFIR safety basis. Evaluate safety-basis impacts of proposed plant changes and modifications and write technical justifications supporting those
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comparative research across Mojo, Julia, Rust, and vendor toolchains. Basic Qualifications: Ph.D. in Computer Science, Computer Engineering, or related field. Experience with LLMs or agentic AI frameworks
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, materials, tools and methods involved in the construction or repair of buildings and other structures. Knowledge of the NEC code book. General knowledge of lock-out/tag-out and relating procedures. Strong
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: The design and analysis of computational methods that accelerate AI/ML when applied to large scientific data sets; Energy efficient physics-aware algorithms, capable of distributed learning on high performance