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scientists and communicate findings through manuscripts and newsletters. Learning Objectives: The candidate will have an opportunity to gain experience in plant genetic analysis, pipeline development, trait
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spectrometry, dynamic mechanical analyzer, thermogravimetric analysis, differential scanning calorimetry, and scanning electron microscopy Working knowledge of statistical analysis, data handling and data
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: Conducting phenotype trials related to maize breeding and genetics. Marker-trait association analysis and RNA-Seq analysis to identify genetic variations associated with desirable traits Preparation of data
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characterization Knowledge of instruments including nuclear magnetic resonance, Fourier-Transform infrared, mass spectrometry, dynamic mechanical analyzer, thermogravimetric analysis, differential scanning
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digestive diseases, primarily acidosis and ketosis. Under the guidance of a mentor, the participant will perform RNA extraction, quality control, RNA sequencing library preparation and data analysis. Learning
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metabolic changes during these events. Techniques may include using GC and LCMS for metabolic flux analysis using pathway labelling as well as analysis of changes in peel surface polymers due to cold chain
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molecular and -omic methods. Learn advanced statistical and bioinformatic analysis of bulk and single cell transcriptomic data. Develop methods to link food nutrition environment with health outcomes. Learn
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, geospatial modeling, and statistical analysis to characterize land use trends related to the northeast agricultural industry and identify impacts and opportunities of agricultural land use transitions
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Force School of Aerospace Medicine is offering a faculty research fellowship at the University of Cincinnati. What will I be doing? The Aerospace Medicine Studies and Analysis Support Branch of the U.S
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routinely collaborates with other U.S. Government agencies, allied nations, and academic research institutions. Our work directly impacts the DoD and MHS by: Providing real-time analysis, at the genomic scale