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Field
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of machine learning for healthcare and related topics Deep knowledge of multi-modal learning, transfer learning, foundation models, and self-supervised learning. Experience in dealing with large
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to systematically understand cancer biology, identify diagnostic and prognostic biomarkers, and improve cancer therapy. Projects will involve the development of AI solutions, including machine learning, deep learning
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. Wildfire, climate change, and ecosystem management Our lab works at the intersection of theoretical and empirical science. We value applicants who combine strong quantitative skills with a deep interest in
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: Design and implement AI/ML pipelines for multi-omics data integration, including supervised and unsupervised learning methods. Develop deep learning architectures (e.g., variational autoencoders, graph
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University of North Carolina at Charlotte | Charlotte, North Carolina | United States | about 20 hours ago
skills acquired in their doctoral programs or to learn new research techniques, in preparation for an academic or research career. In the process of further developing their own research skills, it is
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aspects of machine learning and deep neural networks Free Probability aspects of Quantum Information Theory. While excellent candidates with other research interests might be considered, priority will be
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 7 days ago
developing software to support AI-driven techniques to rapidly diagnose, track, and treat neurodisorders. Responsibilities for this position includes:* Developing deep-learning approaches for insight and
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management Cognitive radio or adaptive communication systems, including dynamic spectrum access, band selection Heterogeneous network architectures, including terrestrial and non-terrestrial networks Deep
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and Education (CODE) section, at University of Twnete, working closely with AI specialists and data scientists. Methodologically, you will explore advanced deep learning approaches, including
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networks and transformers. Practical experience with density functional theory (setups, convergence, interpreting outputs). Strong Python and deep-learning stack (preferably PyTorch); good software practices