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, gaining exposure to public health research, evaluation, and scientific operations that support agency priorities. The participant will strengthen skills in epidemiologic methods, scientific literature
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organizational and program objectives. Learn qualitative research methods through conducting in-depth interviews with internal stakeholders and staff. Build analytical skills by performing gap analyses and
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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
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learning methods for process automation within SharePoint and Microsoft enterprise platforms. Under the guidance of a mentor, the participant will explore research approaches related to large language model
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for the SRM. Learn methods for evaluating and improving performance metrics associated with the SRM tool. Build program evaluation skills by helping design and implement processes to assess the return
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evaluation methods used to strengthen public health initiatives serving rural communities. The learning experience may include participation in Action Plan implementation activities through the Action Plan
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preparation, method optimization, data acquisition, and interpretation of surface and cross-sectional characteristics. Apply AFM-IR/nanoIR and other nanoscale characterization approaches to investigate local
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provides policy making, program direction, coordination, liaison, and expert advice to agency leadership and programs in support of FDA's science-based regulatory efforts. Research Project: You will
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-preventable diseases, respiratory illness prevention, immunization communication, and audience-centered public health messaging. You will strengthen skills in qualitative and quantitative research methods
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analytical methods in the R programming language. Stipend $85,000.00 – $95,000.00 Yearly Point of Contact Rachel Eligibility Requirements Degree: Doctoral Degree. Discipline(s): Computer, Information, and Data