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Researcher position. The selected candidate will work on research and development for testing, analysis, and integration of AI and machine learning (ML) algorithms in new simulation and training architectures
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stimulation (TMS), and transcranial direct current stimulation (tDCS) through interdisciplinary collaborations. Behavioral measurements and physiological recordings, including facilities for computer-based
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machine learning and data harmonization approaches for water quality monitoring and assessment. Responsibilities include developing and validating computational methods to standardize and infer nutrient and
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 13 days ago
-series. Experience exploring machine learning and deep learning techniques for geospatial applications is highly desirable to effectively engage with Earth observation foundation models. Technical
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and operations in the following areas: Soil & Groundwater; Deactivation & Decommissioning; Tank Waste; Robotics; Machine Learning; Artificial Intelligence; Cybersecurity; and Advanced Manufacturing
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Organization U.S. Department of Defense (DOD) Reference Code USAMRDC-MRIID-2026-0008 How to Apply Click on Apply at the bottom of the opportunity to start your application. Description
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, machine learning, and data science. The successful candidate will work with Dr. Yohanna Mejia Cruz on the development of sensing and computational frameworks to characterize human mobility and behavior
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microscopy, tissue culture, biochemistry, physiology, or biophysics. Enthusiasm and commitment to pursue novel research areas and develop new techniques and approaches. Team player with an interest in learning
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., USRP) prototypes. The Bradley Department of Electrical and Computer Engineering has both Electrical and Computer Engineering Ph.D. programs. Both programs continuously rank top in the U.S. The latest
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Chekouo and his collaborators within and outside the University of Minnesota. The research will focus on the development of Bayesian statistical/machine learning methods for the data integration analysis