34 data-visualization-analysis-"https:" Postdoctoral positions at Iowa State University
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that function as artificial enzymes. Responsibilities include conducting experimental research in organic chemistry and catalysis, analyzing data, preparing manuscripts, and contributing to ongoing projects
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for designing and conducting experiments, analyzing and interpreting data, preparing manuscripts for publication, and presenting research findings. The successful candidate will be expected to independently
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not expected. Experience with scientific programming, quantitative data analysis, numerical calculations and simulations using tools such as LabVIEW, Python, MATLAB, Mathematica, Origin, COMSOL
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Qualifications: Experience in proteomic data analysis and laboratory bench work related to proteomics. Experience operating image-based flow cytometry platforms and analyzing resulting datasets. Experience
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the instrumentation, developing the data analysis methods, and applying the methods to study model biomolecules and biomass systems. This research is part of the laboratory’s work for an Office of Science, Biological
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diffraction data, including phase identification and qualitative and quantitative phase analysis using Rietveld refinement. Experience acquiring and interpreting data from one or more of the following
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: Plasma reactor design and operation Experimental diagnostics and system integration Chemical, materials, or spectroscopic analysis techniques Record of scholarly productivity, evidenced by peer-reviewed
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originality Experience in designing, running, and analyzing data from image-based physiology and single-cell activity profiling Preferred Qualifications: Experience with cultivation techniques for plant, fungal
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: Plasma reactor design and operation Experimental diagnostics and system integration Chemical, materials, or spectroscopic analysis techniques Record of scholarly productivity, evidenced by peer-reviewed
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research assistants to ensure high-quality data and sample integrity. The Postdoc will perform bioinformatic and statistical analyses of microbiome, soil, and plant datasets using tools such as R or Python