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applications, and ability to quickly learn and master various computer programs. Must be technically rigorous, organized, and have demonstrated excellence, innovation, and productivity in research. Ability
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: Onboarding and Integration, and Innovation and Creative Capacity. The work will focus on investigating how hybrid work arrangements influence learning, collaboration, innovation, and organizational performance
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county across the state, Texas A&M AgriLife is uniquely positioned to improve lives, environments and the Texas economy through education, research, extension and service. Click here to learn more about
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characterization, and machine learning. The role offers the opportunity to leverage Argonne’s world-class scientific capabilities and engage with a strong network of internal and industry collaborators. Position
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Science, Electrical/Computer Engineering, or a related field by the start date, with a strong publication record in computer vision, multimodal learning, or vision–language models. We require hands-on expertise with
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laboratory integrates, under one roof: Chemical synthesis and drug formulation laboratories High-field 7 Tesla MRI Two-photon and confocal microscopy State-of-the-art electrophysiology Brain-machine interfaces
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, including phase transitions, nonlinear dynamics, morphogenesis, evolution, and chromatin physics. We tackle these questions from fresh angles, using everything from physics and machine learning to long-term
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. Interest and experience with training/fine-tuning machine learning models would also be appreciated. Interest and knowledge of economic theoretical modelling would be a plus. Strong coding skills and
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 14 days ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward
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. Demonstrated expertise in using machine learning and optimization frameworks in conjunction with FE simulations to assist with component and/or process design is preferred. Excellent written and oral