83 parallel-and-distributed-computing-phd Postdoctoral research jobs at University of Minnesota
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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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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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domains. Qualifications Required Qualifications: A recent PhD in a mathematical or scientific field (e.g., physics, computer science, statistics, applied mathematics, or a related quantitative discipline
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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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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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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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a Distributed Hydrology Soil Vegetation Model (DHSVM), produce calibrated simulations, and validate the model for the MEF. The research includes: empirical investigation of high-resolution soil data
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School has three campuses. A four-year MD program and the MD/PhD program are located on the Twin Cities campus in addition to MD programs at regional campuses in Duluth and St. Cloud. Apply for Job
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described species. The Department of Entomology offers a graduate program in Entomology (MS and PhD) and an undergraduate minor in Insect Science minor. These programs provide entomological training
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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