158 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" Fellowship research jobs in United States
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at https://puwebp.princeton.edu/AcadHire/position/40281 and submit a current curriculum vitae, research statement, and a cover letter. Contact information for three references is required. To learn more
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degradation that could occur to C-130 crew members from extended exposure to environmental insults. This research will also inform the development of effective human-machine systems and healthy Airmen protocol
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technological change driven simultaneously by digitization, the application of artificial intelligence and machine learning to all facets of company, economic, and human data, and a new emphasis on the importance
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: N/A Department: Elec, Computer & Bio Engr Campus Location: Job Description Summary: The search will remain open until the position has been filled
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are template based and all US measurements are auto-populated into templates for increased accuracy and efficiency. Fourteen fellowship-trained, subspecialized expert faculty perform both image interpretation
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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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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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California State University, San Bernardino | San Bernardino, California | United States | about 1 month 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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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