72 parallel-and-distributed-computing-phd Postdoctoral positions at University of Minnesota
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computing (HPC) and parallel processing to enable the analysis of massive datasets. Experience in advanced statistical inference (e.g., Bayesian statistics, spectral methods) for extracting robust patterns
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: A recent PhD (or MD) degree in a relevant discipline, such as decision science, health services research, epidemiology, applied mathematics, or industrial engineering. Expected graduation in Spring
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closely related fields · Experience and proficiency with CLM and/or other distributed hydrologic models, and a strong computational and programming background · Ability to work
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results, and discussions of possible new approaches with team members and the Principal Investigator. Qualifications Required Qualifications: PhD in Statistics, Biostatistics, Computer Science, Electrical
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environment, to assist trainees, and present their work in shared lab meetings and external meetings. Highly motivated individuals holding a PhD degree, preferably in virology, are encouraged to apply. The lab
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%) Code in Matlab or other program for stimulus generation, presentation, and analysis, as well as data analysis. (25%) Prepare data for publication and presentation via peer-reviewed journal articles and
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immigrant populations. Founded in 1888, the University of Minnesota Medical 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
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students, and generate reports or publications. Qualifications Required Qualifications: -PhD (must have defended thesis) -NK cell biology and gene editing. -Experience with CRISPR editing also required
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applications. Perform experiments, data analysis, interpretation, and presentation. Report progress regularly. Write manuscripts and grant applications. Qualifications Requirements Completed PhD and have
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, Epidemiology, Computer Science, or related field -Highly qualified and motivated investigator (PhD, or MD/PhD) Preferred Qualifications: -Experience in statistical methods and analysis using SAS, R, or STATA