22 algorithm-development-"Multiple"-"Prof"-"UNIS" positions at University of Maryland
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medical or religious exemption will result in the offer of employment being rescinded. Position Summary/Purpose of Position: Multiple opportunities are available for post-doctoral research at the Institute
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and mentors, and present and obtain feedback on their own research. Especially, the postdoc will have access to multiple industry partners with EHR, claims, and mobile health data, as well as the AIM
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by DOE Energy Efficiency and Renewable Energy to develop carbon molecular sieve (CMS) hollow fiber membranes for alkane dehydrogenation membrane reactors. The position will be in Professor Chen Zhang’s
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serve as the Fellow’s primary mentor with support from other CfME and TLPL faculty. These faculty will meet with the Fellow regularly to provide guidance on developing a pedagogically sound teaching
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(developed from satellite observations, ground monitors, and numeric modeling) with adverse birth outcomes including low birthweight, preterm birth, hospital service use, and mortality. Essential Functions
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of these positions will be electrochemical modeling of high-energy cells. The successful applicant will develop detailed physics- and chemistry-based transient mathematical models for cell charge and discharge, based
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-distance collaborative autonomy; ● Autonomous Unmanned Systems: aerial robotics, robot motion planning, digital twin development, robot navigation of complex terrain, and simulation-based verification
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electrochemical modeling of high-energy cells. A particular emphasis will be on high-rate charging of this cell, at several temperatures. The successful applicant will develop detailed physics- and chemistry-based
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to transform the way society understands and manages the environment. By conducting cutting-edge research into today’s most pressing environmental problems, we are developing new ideas to help guide
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, land use, the built environment, urban form, and transport infrastructure. A key task will be to develop an enhance ridership model using new data sources and through the application of geospatial