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the various aspects of clinical and translational research projects, including study design and development, IRB submission, informed consent, data collection and analysis, abstract submission, data
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groups, photovoice). Skilled in qualitative data analysis and community-based approaches. Demonstrated commitment and ability to collaborate across disciplines to address complex challenges. Strong
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the hydrology field, including analysis and interpretation of large datasets using various analytical, statistical, and numerical techniques. Contribute to the publication of scientific papers and presentation
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the hydrology field, including analysis and interpretation of large datasets using various analytical, statistical, and numerical techniques. Contribute to the publication of scientific papers and presentation
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documentation, and version control workflows. Assist in obtaining and maintaining data use agreements and ethics approvals. Maintain and disseminate scientific resources using best practices for reproducible and
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experience. Responsibilities Perform research in the hydrology field, including analysis and interpretation of large datasets using various analytical, statistical, and numerical techniques. Contribute
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of sedimentology, geochemistry, and microbiology, and general use of destructive, extraction-based methods that are designed for the analysis of large sediment samples. These bulk methods are quantitative but
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Campus in North Austin. Purpose This position aims to advance innovative processing and acquisition methodologies for land DAS and dense surveys, focusing on synthetic modeling, 4D analysis, and seismic
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Institute (LRI) and the Institute for Urban Research and Policy Analysis (IUPRA) at UT Austin seek four postdoctoral research fellows in public policy. The ideal candidates will have a track record in both
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of research productivity. Experience managing the development, implementation, analysis, and write-up of quantitative research. Strong interest and involvement in civil society and democracy. Strong statistical