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postdocs. Research areas include wireless communication, optical communication, coding theory, information theory, and machine learning. We value diversity and believe that a mix of backgrounds and
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Modelling, Electrochemical engineering, Power Electronics, Electrical engineering, Meso-scale modelling, Electrolyzer technology , Machine Learning, Artificial Intelligence Expertise with the following
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biogeochemical properties, including soil carbon fractionation and microbial community Experience with process-based modeling, statistical modeling, or machine learning A proven track record of research
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networks; experience in applying machine learning models and processing imagery from UAS and satellite platforms. Other requirements: Willingness to work irregular hours and in occasionally adverse weather
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expertise to investigate individual differences (that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not
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machine learning, particularly for multi-variable simulations. • Knowledge of complex systems modeling applied to urban dynamics. • Publications in scientific journals. Personal and Organizational
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | about 1 month ago
machine-learning applications for astrobiology. Areas of investigation include: - Living systems detection and characterization - Mars sample return science and tools. - Standards of evidence in life
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university community welcomes differences, encourages open-minded exploration and courageous thinking, and upholds freedom of expression. Ohio State is a dynamic community where opportunity thrives, and
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value for and with society. Whether our contributions come in the form of excellent research, innovative solutions, education or learning, we must make a positive difference to society and contribute to a
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description