682 machine-learning-"https:"-"https:"-"https:"-"https:"-"https:" Postdoctoral positions in United States
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modeling and networked biological systems. You will work at the intersection of high-performance computing (HPC), computational biophysics, and machine learning, leveraging leadership-class computing
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/26. Preferred Qualifications: Strong background in algorithms, human-computer interaction (HCI), and software engineering. Experience in robotics systems, interactive or intelligent systems, or AI
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 4 hours ago
to advance use of foundation models for Earth Science research and applications, including fine-tuning experiments Use of remote sensing, models, and/or machine learning to further our understanding
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Job Description This position offers a unique opportunity to work on advanced projects at the intersection of AI, machine learning, computer vision, and multimodal learning, focusing on advancing
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track record in neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches
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of natural language processing, machine learning, artificial intelligence, and human-computer interaction. Established within the School of Computer Science, LTI pioneers innovative approaches to understanding
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neurobiologically mechanistic modeling (i.e., models should incorporate known neurobiology and neurophysiology, rather than relying on black-box machine learning approaches). * Demonstrated track record in multiscale
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) Assist in original research on clandestine printing networks using computational tools Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals
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Senior Postdoctoral Associate Required Qualifications: (as evidenced by an attached resume) Doctoral Degree or foreign equivalent degree. Three years of research experience in machine learning and
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Infrastructure Sciences Division. Machine learning (ML), specifically deep learning (DL), has been demonstrated to successfully predict the weather for 1-14 days with skill on par with numerical weather prediction