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Field
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. Must be able to work at a computer for extended periods for data analysis, and manuscript preparation. May require routine work with biological samples, including recombinant proteins and antibodies. Use
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demand across agricultural and urban land uses. The team will (1) develop and validate locally relevant methods to estimate consumptive use across mixed urban–agricultural landscapes, (2) estimate
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methods for evaluating AI tools for public health applications; participate in the development and testing of AI-enabled analytical approaches, prototypes; examine how AI can help integrate and interpret
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through their work. You should demonstrate: Interdisciplinary research expertise related to science studies and science policy Strong knowledge of quantitative research methods and their application in
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, and maintain analytical pipelines and software; Evaluate emerging bioinformatics tools, datasets, and analytical methods; Collaborate on scientific presentations, technical documentation, posters, and
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Microsoft Office applications and familiarity with statistical analysis methods and software. Critical thinking and scientific judgment to evaluate complex environmental health data and site conditions to
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, identify populations at increased risk for severe respiratory illness, estimate disease burden, and inform public health planning and decision-making. As part of this research opportunity, you will gain
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insights gained throughout the appointment, you will learn to apply evidence-based methods to develop a research-prioritization matrix and collaborate on a draft a five-year research agenda for the ATSDR
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scientific methods and analytical approaches used to support the mission of the ATSDR Office of Innovation and Analytics (OIA). You will gain hands-on experience with computational, data science, and other
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& Responsibilities: Design and implement methods to maximize the expression and activity of integral membrane metalloenzymes and associated proteins. Carry out activity assays using a GC-MS. Measure metal