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Field
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clinical shadowing experiences. Research topics range from machine learning, designing, and evaluating clinical decision support content to disintermediate scarce medical consultation resources, evaluating
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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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27710, United States of America [map ] Subject Areas: Statistics / Statistics Biostatistics / Biostatistics and Data Science Data Science / Machine Learning Appl Deadline: none (posted 2025/02/12
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Postdoctoral Research Associate - Improving Sea Ice and Coupled Climate Models with Machine Learning
to develop hybrid models for sea ice that combine coupled climate models and machine learning. Our previous work has demonstrated that neural networks can skillfully predict sea ice data assimilation
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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, Economics, or a related field, earned within the past six years Strong computational and statistical skills Experience with large-scale data analysis and machine learning Proficiency in scientific programming
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based on predictions from statistical and machine learning models Postdoctoral scholars are represented by UAW 4121 and are subject to the collective bargaining agreement, unless agreed exclusion criteria
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-based sensor data to enhance the prediction of peatland soil properties and functions. You will focus on leveraging machine learning/deep learning techniques along with explainable artificial intelligence
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Application in CFD/FEA: Develop and apply AI and machine learning methods to derive more generalized and predictive models from existing CFD and FEA results. The goal is to enhance the understanding of stenting
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for energy yield prediction. - Develop models for performance loss rate analysis. - Conduct time series analysis and apply machine learning techniques to assess PV and energy storage system performance