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such as systematic literature review, study design exploration, and analysis of environmental health and exposure data related to microplastics, nanoplastics, and associated chemical contaminants
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apply? Through active involvement in these projects, you will further develop your skills in data collection, analysis, and interpretation related to toxicants, resuscitative adjuncts, and biological
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the network. You will also participate in the analysis and interpretation of respiratory virus hospitalization surveillance data, including applying epidemiologic and statistical methods, developing
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genomic data analysis to identify resistance alleles associated with insecticide and fumigant exposure. Learn how environmental and ecological variables influence insect population expansion and resistance
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collaborators as well as USDA-ARS scientists. The participant will have the opportunity to expand knowledge and expertise in experimental design in vitro and in vivo research data analysis data interpretation
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management, with experience in statistical analysis and process-based modelling. A person with training in database development and management, as well as experience and interest in modeling soil carbon
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USDA-ARS Molecular Biology Postdoctoral Fellowship in the Natural Products Utilization Research Unit
involving physiological, biochemical, and molecular experiments and the use of numerous techniques such as RNA-seq, data mining of DNA and protein sequence databases, analysis of gene function via CRISPR/Cas
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organizational goals. Designing and implementing a public health program evaluation using quantitative and qualitative data analysis methods. Reporting evaluation results using data visualization techniques
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increasing capabilities relevant to biology - raise important scientific, operational, evaluation, and biosecurity questions. You will engage in applied research and analysis examining the potential uses
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, implementation and evaluation science, program evaluation, mixed methods research and evaluation, applied prevention research, systematic literature reviews, data analysis, data interpretation and visualization