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or process modeling, deep learning for spectroscopic data or image processing, and applications of physics-informed machine learning. You would also like to have experience in automating data generation chains
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 3 days ago
machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. The ideal candidate will have a
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and learn independently and perform research with a high level of integrity. Well-organized and able to coordinate and interact with a team of other postdoctoral scholars, graduate students, research
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our state and the world. These partnerships facilitate research and discovery, teaching and learning, and outreach and service. Additional information about the department can be found at http
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Science, Biostatistics, or a closely related area. Strong ML/deep learning foundation plus expertise in at least one of: multimodal learning, time-series modeling, or NLP. Demonstrated working experience
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development through emerging deep learning techniques is of strong interest. The candidate will also evaluate and integrate existing tools and databases into high-throughput pipelines, and facilitate
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-Based Wildfire Smoke and Air Quality Monitoring; Deep Learning for Post-Wildfire Damage Assessment. PROFILE of the OFFICE OF POSTDOCTORAL AFFAIRS (OPA) The mission of the UNLV Office of Postdoctoral
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Preferred: Prior working experience with EHR data, machine learning, deep learning, imaging informatics, and large language models (LLM) is preferred. Prior working experience with popular ML packages, e.g
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). The staff consists of approximately 40 professors and associate professors, in addition to postdoctoral fellows, PhD candidates, researchers, and technical and administrative staff. In total, the Department
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spatial characteristics of TMEs [1]. In lung cancer, several deep learning studies using Haematoxylin and Eosin (H&E) images have demonstrated that the spatial organisation of stromal and immune cell