23 econometric-regional-statistic Postdoctoral positions at The Ohio State University
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, education, consumer or household finance, urban and regional economics, labor economics, or spatial econometrics. Desired Qualifications: The ideal candidate will bring strong analytical skills and experience
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the development of new diagnostics and treatments. You will be part of a vibrant, interdisciplinary ecosystem, collaborating with experts in AI, immunology, genomics, and computational biology at OSU BMI, PIIO, and
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region. Assist in building a coalition of partners and collaborators as part of development of the research proposal. Identify and select problems to be addressed quantitatively with existing long term
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(familiarity with statistical packages for social sciences and scripting tools). Experience working on grant-funded research Experience working with large administrative data sets. Experience collaborating with
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, coding, and analysis skills (familiarity with statistical packages for social sciences and scripting tools). Experience working on grant-funded research Experience working with large administrative data
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in life cycle analysis for separation science are required. Desired: Research interest in statistical data analysis is desired. Strong software skills in Excel and Minitab or other statistical
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Education: Doctoral degree in statistics, biostatistics, or related field. Additional Information: Location: Cunz Hall (0293) Position Type: Term (Fixed Term) Scheduled Hours: 40 Shift: Varying Shifts Final
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data Experience with GIS/RS and database environments (e.g., ArcGIS and Quantum GIS) Experience with machine learning and statistical learning Experience working with large, diverse datasets Familiarity
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research in systematic biology. Skills required include bioinformatics, genomics, data science, artificial intelligence, mCT scanning, knowledge of bat biology, and multivariate statistics. Post Doctoral
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. Particular emphasis to be placed on developing and applying emergent system identification and modal decomposition techniques as well as statistical analysis of large datasets. Candidate will disseminate