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in Machine Learning, Computer Science, Electrical Engineering, Geophysics, Applied Mathematics, or a closely related field. Demonstrated strong research skills, evidenced by high-quality publications
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of life science research experience. · Experience working with mouse models of disease. · Technical aptitude for learning and executing complex experimental techniques for molecular biology, cell-based
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data management and machine learning is also preferred. An interest in energy system topics such as the green transition, sustainable energy systems, digital energetics etc. is preferred. Experience
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 19 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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analyses of ant-collected honeydew; and hyperspectral imaging with machine learning to remotely quantify aphid density and physiological state. The results will be field-validated in the context
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Description Our Commitment Texas A&M University is committed to enriching the learning and working environment by promoting a culture that respects all perspectives, talents & lived experiences. Embracing
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, for their analysis and optimization, we use tools such as artificial intelligence/machine learning, graph theory and graph-signal processing, and convex/non-convex optimization. Furthermore, our activities
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machine learning methods to model changes in the brain over the lifespan, including brain structure and function, and how those changes relate to environment and genomics. What We Offer As an employer, we
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the knowledge acquired during the PhD with team members and acquire new knowledge. - Engage with the Local team at LIPN and the wider national community working on proof theory, programming languages and
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Computing Applications group in CSD has an immediate opening for a Postdoctoral Research Associate to design, develop, and deploy machine-learning and high-performance computing workflows, algorithms, and