5,484 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "U.S" "U.S" "U.S" "U.S" Postdoctoral positions
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the U.S. Department of Energy, collaborate with external partners, and mentor graduate and undergraduate students. This role is designed to provide professional growth toward an independent research career
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separation on commercial recordings and extracting audio features (onsets, pitch, harmony, dynamics); curating datasets; and integrating machine learning approaches to complement rule-based methods. For more
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continued U.S. Department of Energy funding for our group. Department: http://www.physics.ucsb.edu/ School: https://www.ucsb.edu/ Qualifications Basic qualifications (required at time of application
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extensive knowledge on zooplankton imaging techniques ability to program and train machine learning models for automated image classification experience with shipborne campaigns and ready to join multi-week
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, electrical and computer engineering, data science, informatics, biomedical engineering, or a related field. Preferred: Demonstrated expertise in AI-driven drug discovery, machine and deep learning
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-quality students from all 50 states and more than 130 nations. To learn more about ASU, visit https://www.asu.edu/ . All we do at ASU is guided and inspired by the ASU Charter, which reads: ASU is a
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morphology (e.g., geometric morphometrics, machine learning), and phylogenetic comparative approaches. We have: • An engaging, supportive, and collaborative research environment. • Opportunities
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Essential skills, knowledge and experience: Experience with machine/deep learning development Data-Centric AI Knowledge Notions of cybersecurity and networks are optional Spoken and written English Desirable
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community.Together, we’ll create meaningful impact at a scale few others can match. Connections working at The Ohio State University More Jobs from This Employer https://main.hercjobs.org/jobs/22156704/postdoctoral
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Postdoctoral Associate to work on a fascinating project focused on the development machine-learning powered digital twin system for the structural performance of civil engineering structures. The project is a