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Colelcotricum coccodes. (10%) Recording field and greenhouse study data and applying statistical methods (e.g., ANOVA, regression) to correlate pathogen load with disease outcomes and field and greenhouse
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combination of mass spectrometry and NMR methods to assess potential risks of these chemicals to coastal ecosystems. These individuals will also interact with, lead, and assist graduate and undergraduate
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to continue project as well as manuscripts reporting study results. 5% Participation in seminars, journal clubs and symposia. Will present at least 1 formal seminar per year (locally or at a relevant national
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in the University of Minnesota. The research will focus on applying, developing and implementing novel statistical methods for causal inference, integrative data analysis or/and machine/deep learning
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procedures to engineer, manipulate, and phenotype yeast cells. They will perform molecular, genetic, and microbiology techniques and will be expected to select and adapt methods to meet specific research needs