822 parallel-computing-numerical-methods positions at University of Minnesota in United-States
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post doc to analyze genomic, phenomic, agronomic, and environmental data for the breeding program. The Shannon Lab breeds russets, chips, and fresh-market red and yellow potatoes at the tetraploid and
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relevant work experience to equal at least eight years •Experience with qualitative research methods •Experience with REDCap and/or other applications for building and managing online surveys and databases
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health disparities, communication, and/or health systems research Preferred qualifications: Experience with qualitative research methods, including coding and thematic analysis Familiarity with IRB
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research findings in peer reviewed journals. Pursue your own research interests within the broader theme of the position. Data Acquisition Methods and Practice (40%) Support staff involved in neuroimaging
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preferred Capacity for supervisory experience – potential to oversee undergraduate researchers Targeted experience in molecular biology methods, including E. coli culture, cloning, PCR, gel electrophoresis
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(10%): analyzing data and preparing figures of results, presenting data at lab meeting, and drafting methods or results sections for manuscripts. Qualifications All required qualifications must be
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data from multiple sources into a usable and cohesive format, using R to extract, analyze, share, and visualize data. Identifying and applying appropriate analytical methods in consultation with
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research areas include Biomedical and Biological Computation Methods; Devices and Systems; Communications, Signal Processing, and Networking; Computer Engineering, VLSI, and Circuits; Fields, Photonics, and
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or more of the following: ● Experience with urban watershed modeling or lake systems modeling ● Experience with limnological or aquatic field methods ● Experience with statistical methods for making
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on applying, developing and implementing novel statistical and computational methods for integrative data analysis, causal inference, and machine/deep learning with GWAS/sequencing data and other types of omic