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and great opportunity of interdisciplinary training in machine learning and functional genomics. The project combines cutting-edge computational approaches, especially state-of-the-art machine learning
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | about 16 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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implements machine and deep learning programs. Develops algorithms to deconvolve RNA-seq data and compare them to AI-based methods. Performs follow up validation efforts on cell lines. Minimum Qualifications
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for individuals preparing for academic careers in statistics, biostatistics, machine learning, or related fields. The postdoctoral researcher will be supervised by Dr. Razieh Nabi. For inquiries, please contact
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quantitative analyses. Scientific diving certification. Knowledge of hand tools and machine / woodshop tools. Brief Description of Duties: The Postdoctoral Associate will conduct research in the School of Marine
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/or networked microgrids. Brief Description of Duties: The Postdoctoral Associate will assist the Principal Investigator Dr. Peng Zhang in the Department of Electrical and Computer Engineering . The
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of computational biology, Genomics, machine learning, and data science, contributing to the development and evaluation of advanced algorithms for analyzing large-scale biological datasets. This role is ideal
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, machine learning and surrogate modeling; and presentation skills and proven research independence. Application material should include a cover letter, CV, and the names and contact information of three
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Job Description Apply now Req Id: 39404 Job Title: Post Doc Research Associate City: West Lafayette Job Description: Job Summary Postdoctoral Research Associate Position – Machine learning
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on topics including mathematical foundations of data science and statistical/machine learning, with an emphasis on inverse problems and in-context learning for PDEs and interacting particle systems