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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 11 minutes ago
molecules and ions, and solid-phase analogs of cosmic grains, exoplanetary atmospheric aerosols, and protoplanetary disk materials from gas chemistry at low temperature using a plasma expansion. Experience
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Overview: Based in Barcelona, in a collaborative and dynamic international team Experienced computational modeler / mechanical engineer Full-time, continuing role The Postdoctoral Fellow will be
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support and related data capture strategies. Creates project artifacts and deliverables, such as process flow maps, data analysis, models, reports, and develop, organize, and summarize materials
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Intelligence/Computing Natural Language Processing AI for Science and Innovative Applications of AI Autonomous Agent and Multi-Agent Learning Complex Systems Modeling, Control, and Decision Making Computational
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related to the project. Candidate Profile Strong proficiency in software tools related to materials/process modeling, thermochemical systems, statistical design (DoE), and data analysis. Excellent verbal
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, thus achieving a vascular model with a broad range of (relevant) dimensions. To connect the chips to a perfusion pump, it must be contained in a small casing which should be made from materials
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-Quality System Work closely with PhD students and postdoctoral researchers in materials science, physics, and computer science to incorporate latest research results—including new models, representations
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verification of quantum programs. The framework consists of the following deliverables: Intermediate Language. We plan to develop an intermediate language to model quantum programs. The intermediate language
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, including blended cements. Develop curing-specific DoC prediction models and validate against experimental results. Determine the effect of controlled exposure conditions on the DoC of fibre cement materials
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paste content, and study carbonation of recycled cementitious materials in stockpiles. You will develop thermodynamic predictive models trained on the comprehensive database that will be compiled