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evaluations, managing financial requests and Concur reports, efficiently processing orders and invoices, compiling staff statistics, assisting in disciplinary actions, and liaising seamlessly with the Library
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, and statistical analysis *Proficiency with relevant software, e.g. Python, R, and STATA. *Excellent writing and analytical skills *Evidence of ability to take the initiative in solving practical
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addition to these responsibilities, the position entails coordinating comprehensive staff evaluations, managing financial requests and Concur reports, efficiently processing orders and invoices, compiling staff statistics, assisting
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of tools and public goods. Requirements Applicants must have (or expect to have) a PhD in the social sciences, statistics, computer science, or related fields, and their interests must fall at the technical
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coordination. Research on Policing Reform and Accountability project: Contribute to ongoing work on police body-camera research, which may include statistical analysis, literature review, and other research
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the Study Abroad Fair, Passport Day, and other SAP program events; assists with providing reports, statistics, and analyzing study abroad data as needed (e.g., for the nation-wide Open Doors study abroad
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The Center for Statistics and Machine Learning (CSML) at Princeton University invites applications for Postdoctoral Research Associates position. Appointments are for one year with the possibility
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The Department of Operations Research & Financial Engineering (ORFE) invites applications for a postdoctoral position broadly in statistics and machine learning. This position is supported through
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letters) for at least 2 references (we will contact references for letters at a later stage). PREFERRED - Experience working with REDCap - Basic statistics training (e.g., ANOVA, t-tests, use of SPSS and R
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Ph.D. in quantitative ecology, climate sciences, population biology, applied mathematics, statistics, or an equivalent discipline. Additional experience in climate, meteorological, or environmental data