127 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" Fellowship research jobs in United States
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a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more about AI at Princeton, please visit https://ai.princeton.edu
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machine learning. The specific goal is to extend new and existing visualization environments to support efficient and precise annotation of histopathology images using a combination of expert human review
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Computer/Information Sciences Internal Number: A-179059-11 General Description The Johns Hopkins University Data Science and AI (DSAI) Institute welcomes applications for its Postdoctoral Fellowship program
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Qualifications ? Ph.D. in Physics, Materials Science, or a related field with a concentration in electron microscopy methods ? Experience in the collection and processing of TEM/STEM data ? Computer programming
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programming, algorithm development and deep learning model implementation, and practical experience in drone and boat-based surveys are preferred. Background Investigation Statement: Prior to hiring, the final
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California State University, San Bernardino | San Bernardino, California | United States | 3 months ago
, computational, or data-driven approaches-are encouraged to apply. Interest or experience in Artificial Intelligence, Machine Learning, or Big Data Analysis is preferred. Responsibilities include research (85
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. To learn more about ongoing research in Saha laboratory please visit https://www.kumc.edu/school-of-medicine/academics/departments/radiation-oncology/research/saha-lab-of-radiation-biology.html. Job
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). Proficiency with programming languages (e.g., R, Python, Matlab) for data management and analysis, computational social science, and/or machine learning applications. Acquisition, processing, and analysis
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in Spatial Omics and Multi-Modal Data Integration Duties & Responsibilities: Develop computational and machine learning methods for spatial omics data (spatial transcriptomics, spatial proteomics
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, the participant will learn HPC computing technologies and techniques in genomic epidemiology and machine learning to quantify drivers of IAV evolution in swine using data generated from IAV surveillance in human