78 parallel-computing-numerical-methods Postdoctoral positions at University of Minnesota in United States
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computational mechanics, FEM/Particle methods, time integration and analysis (60%); -help to write quality research proposals to government agencies and industry, prepare/assist in journal research papers/book
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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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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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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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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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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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) program. The role offers opportunities for professional development, including leading large-scale analyses with advanced statistical methods and leading manuscript preparation to enhance your research
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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