111 parallel-computing-numerical-methods-"Simons-Foundation" research jobs at University of Minnesota
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Previous Job Job Title Program and Research Assistant Next Job Apply for Job Job ID 370191 Location Twin Cities Job Family Supplemental Employee Full/Part Time Part-Time Regular/Temporary Regular
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development, data management, and preparation of scientific reports (20%) Computer knowledge to enter data from experiments into existing databases; spreadsheets and web-based applications. Conduct background
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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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team of undergraduate/postgraduate researchers. Candidates should be able to multitask parallel evolution experiments with phenotypic and genomic analyses. Job Duties and Responsibilities: Typical tasks
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multiphase flow in porous media. 80% - Applying numerical and analytical infiltration models to quantify groundwater recharge potential under varying hydrogeologic conditions. In parallel, the researcher will
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of Minnesota, working with Professor Russell Funk. This position focuses on using computational methods to understand how knowledge is produced, shared, and applied across complex systems. About the Project
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-doctoral Associate will develop algorithms and theory for machine learning methods, as well as implement and apply ML methods to problems in domains such as computational biology and neuroscience. This is a
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-disciplinary research team, consisting of researchers in coil development, electromagnetic simulation, parallel transmit RF pulse design, pulse sequence development, advanced MR image processing, analysis and
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conducting population analyses on swans, geese, or other birds that exhibit deferred reproduction Advanced experience coding in R, including parallel computation using high-performance computing clusters
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or outside the University of Minnesota. The research will focus on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine