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Postdoctoral Appointee - Uncertainty Quantification and Modeling of Large-Scale Dynamics in Networks
: Expertise in rare event simulation, deep learning, and developing computationally efficient approaches for simulation and modeling in complex systems is highly desirable Experience with parallel computing
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. Experience in high-throughput sequencing data analysis and cluster/cloud computing. Proficiency in variant calling, single-cell DNA and/or RNA analysis, and machine/deep learning (preferred but not required
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development, and publication in peer-reviewed venues. Strong background in machine learning, with research experience in deep learning, foundation models, or related areas. Solid programming ability in Python
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Current Employees: If you are a current Staff, Faculty or Temporary employee at the University of Miami, please click here to log in to Workday to use the internal application process. To learn how
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it is not required that the candidate possess a deep theoretical understanding of AI, practical proficiency in using AI will be considered in the selection process. Applicants must hold a PhD prior to
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reconstruction and processing. Candidates are expected to have a strong background in medical imaging, experience in imaging system evaluation, deep learning and clinical investigation. Brief Description of Duties
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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reconstruction and processing. Candidates are expected to have a strong background in medical imaging, experience in imaging system evaluation, deep learning and clinical investigation. Brief Description of Duties
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, tape-out, and testing, preferably with applications to AI systems ● Design, analysis, and modeling of AI hardware such as deep neural network accelerators or neuromorphic computing. ● Emerging AI
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and novel AI hardware to help solve significant real-world problems using machine learning and deep learning. ALCF researchers work in a highly collaborative environment involving science application