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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 6 hours ago
Title BDC Post-Doc Research Associate Vacancy ID PDS004619 Full-time/Part-time Full-Time Temporary Hours per week 40 FTE 1 Work Location Chapel Hill, NC Position Location North Carolina, US Hiring Range
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, United States of America [map ] Subject Areas: Engineering / Materials Science , Materials Science and Engineering , Mechanical , Mechanical Engineering Appl Deadline: 2025/09/15 11:59PM ** (posted 2025/09/04, listed
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combined total of 5 years of relevant work experience as a post-doc and/or in an R&D position, excluding time associated with family planning, military service, illness or other life-changing events
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-based or machine-learning/AI-based climate modeling (e.g. hydrometeorological and/or atmospheric processes) are particularly encouraged to apply. Position 3 Working with Dr. Kelly Baker , EEH Associate
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Trustworthy AI Systems NSF Research Traineeship , the Digital Trade and Data Governance Hub , the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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Trustworthy AI Systems NSF Research Traineeship , the Digital Trade and Data Governance Hub , the Center for Machine Learning, and the Human-Computer Interaction Lab at UMD, and the Center for Equitable AI and
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for the automatic characterization of roadside edges. Roadside objects can be small (grass strips) and/or three-dimensional (ditches, hedges) and extend over large areas (landscape scale). The post-doc will be
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docking and machine learning. Among the key duties of the position are the following: Performs various research and technical operations relative to ongoing investigatory activities of a laboratory; may
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Description The Clinical Artificial Intelligence Lab at NYU Abu Dhabi seeks to improve patient care by developing new machine learning methodologies that tackle unique computational problems in
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have: Expertise in processing optical remote sensing data over large areas and using machine learning models for predicting ecological metrics Strong background in remote sensing in hyperspectral imaging