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of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate who is eager to work on AI based solutions for predictive inteligence for MRI scanning. The candidate will
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time. Part-time faculty serve in a remote capacity at the University. This position is for the designated college and will report to the Faculty Manager. Part-time faculty teach courses that may begin
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assessment, programming and machine learning. If so, we encourage you to apply! You will develop exposure and physical vulnerability maps for past and future (1970-2100) and integrate these into a flood risk
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total period of four years. The ultimate goal of this trajectory is to obtain a PhD degree. The candidate will work as a doctoral researcher at the facilities of Orsi Academy, Melle, Belgium
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 25 minutes ago
include (but are not limited to): Develop algorithms to characterize aerosol speciation from LIDAR fluorescence signals Develop machine learning emulators to represent forward operators for polarimeter-only
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Nature of position : Temporary, part-time Job Summary/Basic Function: The Department of English and World Languages is in need of temporary, part-time instructors to teach major courses and courses within
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teaching duties. Requirements: Applicants should possess a PhD degree in Computer Science, Computer Engineering, Information Systems, or a related field, and sufficiently demonstrate abilities to conduct
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for machine-brain interface, tissue repair, and disease diagnosis, monitoring and treatment, are especially encouraged to apply. The appointee is expected to conduct world-class research and to teach
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look forward to receiving your application! We are looking for a PhD student for sustainable and resource-efficient machine learning. Your work assignments Machine learning has recently advanced through
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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | 24 minutes ago
to): Develop machine learning algorithms that utilize fire products from geostationary satellites to better represent fire evolution and variability Develop machine learning emulators to represent forward