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issues. Proficiency in urban modeling tools such as MATLAB, Python (especially libraries like Pandas, NumPy, SciPy, GeoPandas, etc.), and R. Advanced skills in predictive modeling and machine learning
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Active Learning Initiative (ALI). The Postdoctoral Associate will participate in ALI programming, including weekly fellows’ meetings and program evaluations such as surveys and classroom observations
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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) | Greenbelt, Maryland | United States | 19 minutes 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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applications for a fully funded postdoctoral associate position. This position, available immediately, focuses on developing machine learning and deep learning methods for analyzing large-scale single-cell DNA
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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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Massachusetts Institute of Technology (MIT) | Cambridge, Massachusetts | United States | 25 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
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project), create a unique opportunity to apply machine learning and neural network methodologies, in conjunction with simplified ice sheet models, to advance understanding of ice sheet basal processes and
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of the distinguished Association of American Universities. Connections working at New York University More Jobs from This Employer https://main.hercjobs.org/jobs/22169817/postdoctoral-associate-x28-tomanik-lab-x29
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quantitative and machine learning approaches ● Developing predictive models linking nuclear features to future cell fate ● Interacting with collaborators in imaging, computational biology, and developmental