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chronic conditions and illnesses. This research aligns with CVDB's public health mission to strengthen evidence supporting improved recognition and characterization of ME/CFS. Learning Objectives: You will
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nucleic acid extractions, amplicon sequencing, data management and analysis. You will learn how to identify risks and improvement opportunities, ensure compliance with established policies and agency
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interdisciplinary research teams on quantitative analyses of complex genomic datasets; Learn to use remote, high powered computer clusters to process large datasets. Mentor: The mentor for this opportunity is Adam
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., Medicare and Medicaid) to examine clinical and environmental factors associated with CKD incidence and trends. Apply advanced statistical and machine learning methods, including semi-supervised cluster
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for respiratory virus genomics; Final projects will be developed jointly by the mentor and fellow. Learning Objectives: During the fellowship, you will learn to: Apply bioinformatics and data science methods
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also receive training in use of Excel spreadsheets, PowerPoint, Visio, and plotting and statistical analysis using various software platforms. Learning Objectives: Under the guidance of a mentor you will
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MMA’s program and regional offices, exposing you to a variety of perspectives and issues. Learning Objectives: Under the guidance a mentor, you may gain skills and experience in the following areas: Gain
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. Learning Objectives: By the end of this training/research experience, you will be able to: Explain the structure and functional organization of plant, insect, and/or fungal genomes and describe how genomic
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in a Unix environment and on high-performance computing equipment. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: (a) apply methods in computational
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identification and application of mitigation strategies. Learning Objectives: Under the guidance of a mentor, you will have the opportunity to learn to: Describe interdisciplinary research approaches in genetics