76 building-physics-"https:" "https:" "https:" Postdoctoral positions at Oak Ridge National Laboratory
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the last 5 years. Preferred Qualifications: Proven RDD&D experience in urban-scale building energy modeling. Experience planning and designing solutions for a variety of community projects, including
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transformative solutions to compelling problems in energy and security. Within ORNL, the Building Envelope Materials Research (BEMR) Group develops and deploys affordable, advanced, and resilient solutions
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Model, working closely with the experimental team to understand this device’s PMI physics and optimize performance of the device in its upcoming campaigns. The position resides in the Power Exhaust and
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manufacturing technologies and manufacturing systems that could achieve transformative gains in process productivity and enhance manufacturing sustainability Strong analytical capabilities with an experience in
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models of gas transport and process behavior in industrial systems Collaborate with a team of scientists from across the national laboratory complex on modeling efforts Extend process flow modeling across
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, patents, journal papers, and conference publications, participate in proposals, and estimate costs. Basic Qualifications: A PhD degree in physics, optical or electrical engineering, nuclear engineering, or
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and Polymer Chemistry Section, Chemical Sciences Division, Physical Sciences Directorate, at Oak Ridge National Laboratory (ORNL). This METALLIC (Minerals to Materials Supply Chain Facility) project
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that combines mechanistic ecophysiology with AI, such as: Physics-informed machine learning and neutral networks to investigate plant physiological / abiotic relationships Bayesian statistics and neural and
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Engineer will conduct R&D in nuclear nonproliferation with expertise in computational nuclear reactor physics through modeling and simulation (M&S). The candidate will perform analysis and methods
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discovery. Research directions may include scientific foundation models, physics- and knowledge-guided machine learning, graph and geometric learning, surrogate and reduced-order modeling, uncertainty-aware