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control) the arrangements of cells in spheroids and tumoroids. The project will primarily involve the development and testing of machine learning techniques based on biophysical simulations to predict
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/modeling, at the Electrical and Computer Engineering Department, and in collaboration with the Applied Mathematics Department, Santa Clara University. This is an in-person position at Santa Clara University
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
. Description: The research for this position will focus on the application of artificial intelligence and machine learning to solving complex trajectory design problems. Specific applications will focus on tour
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groups working on digital health and wellbeing , network science , computational social science , and various topics in machine learning. You will be working in the research group of one of the PIs
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 8 hours ago
, repeatable coverage of fire-prone regions. When combined with modern statistical and machine-learning approaches, these data enable robust mapping of fuels, assessment of burn severity, estimation of biomass
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topics. The department has a strong community on related topics: research groups working on digital health and wellbeing , network science , computational social science , and various topics in machine
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, including development of new computational tools for processing large-scale biospecimen data Creation of novel machine learning frameworks for automated scientific analysis and discovery Design and
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are invited for a postdoctoral position in the Air Quality and Particle Technology group (Prof. Dr. J. Wang). The successful candidate will be hosted by the Institute of Environmental Engineering, ETH Zürich
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in the Earth's outer core, with implications for deep Earth processes [1]. A variety of inverse methods (data assimilation, machine learning, etc.) has been employed to recover the fluid motions in
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | 3 months ago
://doi.org/10.3389/fenvs.2025.1473890 Madani, N., et al., & Miller, C. E. (2024). A machine learning approach to produce a continuous solar-induced chlorophyll fluorescence dataset for understanding Arctic