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for collecting and analyzing urban data (traffic, energy consumption, environment). • Strong skills in integrating IoT devices into complex digital systems. • Advanced expertise in machine learning and
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Institute (https://cse.umn.edu/aiclimate). The role involves building knowledge-guided machine learning (KGML) models for sustainable agricultural practices, developing AI-ready benchmark datasets, and
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rise (SLR) and flooding. Integrate field data (e.g., salinity, nutrient levels, soil and water properties) into the development of numerical models to enhance predictive accuracy. Apply machine learning
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for healthcare. The Alsentzer Lab is an interdisciplinary research group in the Department of Biomedical Data Science at Stanford University. Our mission is to leverage machine learning (ML) and natural
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) in the department and in the Great Plains IDeA-CTR network, and growing institutional strengths in AI, machine learning and clinical informatics. This is a unique opportunity to translate and expand
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National Aeronautics and Space Administration (NASA) | Fields Landing, California | United States | 25 days ago
research using various statistical and machine learning approaches to develop regional and global products and improve our understanding of the drivers of carbon stock changes across a variety of ecosystems
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who qualify for the project. Work with physicians to obtain biopsy study samples. Data Collection, Management and Monitoring (40%) Conduct clinical assessments and documentation, biopsy acquisition, and
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geotechnical modeling, machine-learning model development, structural sensing and health monitoring, conducting physical experiments, and validation of computational models. Required Qualifications: A successful
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basis of age (40 and over), color, disability, gender identity, genetic information, marital status, domestic partner status, military or veteran status, national origin/ancestry, race, religion, creed