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University of California, Los Angeles | Los Angeles, California | United States | about 2 months ago
Chemical Engineering, Biomedical Engineering, Bioengineering, Synthetic Biology, Immunology, or a closely related field. • Technical Expertise: Proven track record of designing, executing, and interpreting
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requirements We will consider applications based on the following skillsets. If you do not fullfil all requirements, please still apply (there is room for learning-on-the-job). A PhD in aerospace/mechanical
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world and a suite of associated machine learning tools. The incumbent will be advised by Dr. Laurel Symes (CAPS, [email protected]). Depending on the research direction, collaboration and additional
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National Aeronautics and Space Administration (NASA) | Merritt Island, Florida | United States | about 12 hours ago
. These one- to three-year fellowships are competitive and are designed to advance NASA’s missions in space science, Earth science, aeronautics, space operations, exploration systems, and astrobiology
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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addresses themes such as sustainability transitions, environmental governance, citizen science, ecological justice, social learning, climate adaptation and relationships between science, policy and society
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& Computation: designing efficient quantum algorithms and theoretical frameworks across diverse hardware platforms to address problems in quantum error correction, learning, control, measurement, simulation, and
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been at the forefront of innovation, and our ambitious Vision 2028 strategy underscores our commitment to reshaping education for the benefit of all. We empower students from all backgrounds with
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designed to study the coupled evolution of hydrology, weathering, ecosystems, and biogeochemistry. The successful candidate will advance ERT as a central observational platform for monitoring water movement
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and machine-learning potentials for planetary materials. Curating and generating large-scale ab initio datasets across wide pressureâ“temperature regimes. Designing and training advanced machine