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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 19 hours ago
Chapel Hill seeks a talented, highly motivated postdoctoral researcher with a background in machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 8 days ago
Chapel Hill seeks a talented, highly motivated postdoctoral researcher with a background in machine learning and/or biomedical image processing. The ACM Lab is developing software to support AI-driven
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, physics, materials science, applied mathematics or a related field -experience in building LLM-based agent systems -strong background in image processing and analysis, including deep learning -experience
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degree of autonomy. Influences others regarding policies, practices and procedures. Essential Functions: 60% of Time the Research Scholar may: • Lead development of deep learning algorithms capable of
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. Experience in training deep learning-based models on HPC-resources is also meriting. A well-established international network in EM, and earlier international collaborations around research infrastructure
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economically viable, which requires reduced acquisition time, advanced scanning strategies, and novel reconstruction and defect-detection algorithms based on artificial intelligence and machine learning (AI/ML
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(ER-C) and to the Jülich Supercomputing Centre. Within IAS-9, the Deep Learning for Electron Microscopy group develops machine learning methods grounded in the physics of the measurement rather than in
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well as civic organizations and authorities interested in improving civic participation. The research in this postdoctoral position focuses on text and image feature learning for news ecosystems
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(e.g., R, Python, Perl, C++, Java) and knowledge of statistics and mathematics are required. Training experience in artificial intelligence (such as machine learning and deep learning), experience
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-omics. • Machine-learning approaches including feature extraction, clustering, predictive modeling, deep representation learning, changepoint detection, or generative modeling. • Workflow automation