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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | about 2 months ago
FELLOW-TRANSPORTATION, MIT Climate & Sustainability Consortium (MCSC) Impact Fellowship Program, to assume a position for an individual with transportation expertise who wants to transcend academia and
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goal of this project is to develop machine learning techniques to improve climate change simulations. M²LInES is composed of more than 30 scientists at NYU, Columbia, MIT, Princeton, GFDL, NCAR and CNRS
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cultural atmosphere and world-class sports and entertainment in a temperate climate. Minimum Qualifications: Ph.D in a related field. Preferred Education and Experience: Ph.D. in biology, neuroscience
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We are looking for an Associate Research Scientist to work at the interface between climate modeling and machine learning. The successful applicant will perform research as part of the multi
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with oceanography, carbon cycling, and/or ocean and climate models is preferred. The successful candidate will have the opportunity to build strong links with the many collaborators supported across two
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work will connect with the CliMA initiative at Caltech, MIT and NASA JPL, and the multi-university ONR initiative on sea ice , which both aim to sharpen our understanding of the climate using more
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innovation to solve global problems, such as the growing energy demand and climate change. About the Role This position is part of a 5-year PhD degree program offered through Hiroshima University (HU
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facing a period of significant changes and uncertainties driven by a variety of factors (technologies, mobility norms, climate changes, energy crises). The conventional approach to transportation planning
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climate change. About the Role Our Postdoctoral Fellow positions typically run for three years with the potential for a 2-year extension based on job performance, funding availability, and ongoing
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the field of Environmental Health, Climate, and Migration data. Their primary focus is ensuring the quality and availability of acquired data. Working closely with data scientists, analysts, and stakeholders