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, Cybersecurity, AI, Machine Learning (ML), Data Science, or another closely related subject, no more than three years before the application deadline; has documented knowledge of AI and ML; has demonstrated
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Postdoc Position for Computational Genomics, AI Precision Oncology, Cancer Biology and Immunobiology
University of Pittsburgh, Pittsburgh , Pennsylvania, US | Pittsburgh, Pennsylvania | United States | about 2 months agomechanism-driven AI and agentic AI frameworks (iGenSig-AI, G2K) that integrate biological knowledge with cutting-edge machine learning to transform omics data into actionable therapeutic insights
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data and multimodal datasets combining imaging and molecular measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with
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. Possible research topics include: Scientific machine learning Numerical partial differential equations (PDEs) Computational fluid dynamics Neural operators High-performance computing Data-driven
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, creative start-ups, big data, big ambitions, hands-on learning, and a whole lot of robots, CMU doesn’t imagine the future, we invent it. If you’re passionate about joining a community that challenges the
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datasets with multi-omics profiles of tumors from previous studies Developing new machine learning models through collaboration with the Swiss Data Science Centre Establishing data analysis pipelines
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. Geometric or variational approaches to partial differential equations. Differential, metric, or algebraic geometry. Invariant theory or symmetry-based methods. Geometric data analysis. Scientific machine
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of Virginia (UVA) School of Medicine is seeking to fill Postdoctoral Researcher positions in computational biology and machine learning. The lab develops modern machine learning, generative modeling, and
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measurements. Have a background in computational biology, bioinformatics, or machine learning for biological problems. Have experience with modern deep learning frameworks (PyTorch, JAX, or equivalent). Have
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on development of novel computational methods with state-of-the-art machine learning for gaining fundamental insights into healthy and diseased human tissues of the heart, cardiovascular system, and