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with advanced analytical instrumentation such as HPLC, texture analyzers, FT-IR spectrometers, zeta potential and particle size analyzers, differential scanning calorimeters, and supercritical CO2
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performance. Through this experience, you will gain hands-on exposure to analytical instrumentation, quantitative analysis, and modern predictive modeling techniques. Learning Objectives: Under the guidance
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enrichment NGS, metagenomic NGS. Participating in studies to characterize assay performance, including analytical sensitivity, reproducibility, inclusivity, and other relevant performance characteristics
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disease burden. Using these findings, you will learn to develop analytic approaches and research products that strengthen the Registry's ability to monitor ALS nationally and support evidence-based research
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understand the burden of diabetes and inform public health strategies to improve prevention, treatment, and health outcomes. Learning Objectives: Through this training opportunity, you will have the
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. Learning objectives include: Fractionation and purification of biomass into isolatable compounds for downstream development into products. Data organization and data management skills. Utilizing analytical
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to CDC's efforts to better understand the burden of CKD and inform public health strategies to improve prevention and outcomes. Learning Objectives: You will have the opportunity to advance your skills
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software and analytical tools. Gain exposure to specimen review, quality control, and laboratory data documentation practices used in molecular diagnostics. Learn quality management principles and practice
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of multiple surveillance and administrative data sources. Development of reproducible analytical workflows using programming languages such as R and Python. Application of machine learning and predictive
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scales, from the genome to the continent, and sub-daily to evolutionary time scales. One of the goals of the SCINet Initiative is to develop and apply new technologies, including AI and machine learning