745 parallel-computing-numerical-methods 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 Research Engineer - Research Computing (Research Professional 2) Next Job Apply for Job Job ID 369733 Location Twin Cities Job Family Research Full/Part Time Full-Time Regular
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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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resources, to education and training in computational methods and technologies. Pay and Benefits Pay Range: $85,000 - $105,000; depending on education/qualifications/experience Time Appointment: 100% Time
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design and site selection methods, be able to work independently and leading small teams, and have strong interpersonal skills. Past relevant experience designing and coordinating wildlife surveys is
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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