2,027 parallel-and-distributed-computing-"UNIS" Postdoctoral positions in United States
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turbulent combustion applications, as well as parallel scientific computing. Knowledge of deep machine learning (using TensorFlow, PyTorch, etc.) for multi-fidelity modeling, regression tasks, management and
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Postdoctoral Associate position on Informatics, Data Science and Machine Learning in the Dr. Ohno-Machado Lab at Biomedical Informatics and Data Science, Yale University Job Description Under
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management machine learning, distributed computing, and resource optimization leveraging the unique computational resources available at ORNL, including the Frontier supercomputer—the world's first exascale
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. Expected distribution of duties includes: ● Laboratory benchwork: 75% ● Data analysis, writing, and presentations: 25% Qualifications Required Qualifications: ● A PhD degree in Neuroscience or a related
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Program Department: Medicine | Pathology Post Doctoral Scholar position in the laboratory of Hiroki Taniguchi Ph.D. Research focus is on understanding the molecular, cellular, and circuit mechanisms
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therapies for the treatment of corneal diseases. One research program investigates the microenvironment/niche of human limbal stem cells to elucidate those factors that govern the fate of limbal stem cells
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with Python and R programming languages. Experience with functional genomic technologies including massively parallel reporter assays. Biomedical informatics or biomedical research experience. Preferred
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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parallel computing techniques including working in the cloud. Preferred Qualifications Education: No additional education beyond what is stated in the Required Qualifications section. Certifications
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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