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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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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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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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looking for postdoctoral researchers in the area of computer vision, AI, and machine learning. The initial appointment will be for 2 years with a possible extension with a tentative start date in January
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prioritize candidates who specialize in Artificial Intelligence (AI) and Machine Learning (ML), including but not limited to areas such as deep learning, natural language processing, computer vision
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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