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, and machine learning experience; and (c) some research publication experience. Knowledge of Arabic language is preferred, but not required. Previous work on Arabic NLP is preferred but not required
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organic, or organic-enhanced, new-particle formation in the atmosphere. We will combine our molecular-level model development with machine learning and artificial intelligence methods, targeted validation
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computational analyses of single-cell, spatial transcriptomics, and multi-omics datasets Developing and maintaining reproducible, well-documented analysis pipelines Applying and adapting machine learning and AI
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modeling and model–data fusion techniques, and developing faster, machine-learning–based tools that can stand in for slow model simulations. These tools will be used to test how model parameters influence
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Preferred License: No If yes, what is the preferred licensure/certification?: NA Preferred Computer Applications: Not Applicable Preferred Other Computer Applications: Learning Management Systems, Student
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Scene Understanding Detection and Identification of Objects (SSUDIO) project. The purpose of this project is to develop scene understanding from 3D scans of ships by applying machine learning/computer
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Professor level. The ideal candidate will be at the forefront of research that integrates modern machine learning methods with economic theory and econometric analysis. We are particularly interested in
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that preserves object identity or style. They should have a solid publication record in top-tier computer vision conferences such as CVPR, ICCV, or ECCV, and demonstrate proficiency in deep learning frameworks
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Successful applicants will teach undergraduate, M.H.A, and D.H.A. courses across more than one content area, such as: Healthcare Operations and Performance Improvement Health Economics and Health Policy
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, accurate, and physics-informed machine learning models for predicting blood flow in patient-specific vascular geometries. Current simulation-based approaches require complex 3D meshes and are often too slow