79 parallel-computing-numerical-methods-"Simons-Foundation" Postdoctoral positions at University of Minnesota in United States
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Previous Job Job Title Post-Doctoral Associate - Computational Health Sciences Division Next Job Apply for Job Job ID 360487 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular
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Previous Job Job Title Post-Doctoral Associate - Electrical and Computer Engineering Next Job Apply for Job Job ID 369523 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular
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of Minnesota is seeking highly motivated Postdoctoral Associates to join our team. Funded by the Simons Foundation), these positions offer an exciting opportunity to conduct cutting-edge research
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Previous Job Job Title Post-Doctoral Associate - Computation (Hanany) Next Job Apply for Job Job ID 369600 Location Twin Cities Job Family Academic Full/Part Time Full-Time Regular/Temporary Regular
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Informatics (IHI), University of Minnesota, Twin Cities. Dr. Bayat’s team develops highly scalable and computationally accelerated medical imaging and analysis methods to assist in enhanced diagnosis and
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under the faculty mentor's supervision ensuring all lab members follow all safety protocols, properly recording data from experimental and numerical research and promptly backing up all data. -While in
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research findings in peer reviewed journals. Pursue your own research interests within the broader theme of the position. Data Acquisition Methods and Practice (40%) Support staff involved in neuroimaging
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with innovative modeling methods and data analytics methods and spur cross-discipline development between the team in both water resources and computer science. Specifically, the research projects
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or more of the following: ● Experience with urban watershed modeling or lake systems modeling ● Experience with limnological or aquatic field methods ● Experience with statistical methods for making
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic