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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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National Aeronautics and Space Administration (NASA) | Greenbelt, Maryland | United States | about 7 hours ago
to constrain the representation of aerosols in the NASA GEOS Earth System Model. Activities that would be involved in this project include (but are not limited to): Implement machine learning transfer learning
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intelligence, and multimodal learning. The main objective of this position is to develop novel generative AI methods for computer vision applications, with a particular focus on Diffusion Models and Vision
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on studying the principles of neural computation through recurrent neural networks, dynamical systems theory, and machine learning. - Develop mathematical and computational models of neural networks - Analyze
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at other institutions. The International Union, United Automobile, Aerospace and Agricultural Implement Workers of America (UAW) is the exclusive representative of the postdoctoral scholars. https
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closely related field Required Qualifications: - Experience in spectral CT and uantitative imaging including radiomics, machine learning, deep learning or artificial intelligence as applied to radiology
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National Aeronautics and Space Administration (NASA) | Huntsville, Alabama | United States | 5 minutes 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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modeling, satellite remote sensing, or machine learning Programming skills (Python or R) Strong publication record
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humanities disciplines. Familiarity with contemporary AI systems (e.g., machine learning, generative models) at a conceptual or applied level. Experience with qualitative or mixed research methods (e.g
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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 24 days ago
data analysis methods to study biological memory circuits and their applications to machine learning. Building on recent work from the Fiete Lab, the role focuses on identifying principles of biological