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knowledge gained from climate models to applied researchers and decision makers are encouraged to apply. A Ph.D. in physical geography, statistics/biostatistics, environmental, climate, atmospheric, physical
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econometrics or statistics. Previous experience is desirable with empirical research or comparable private-sector work, with preference given to these and related skill sets: coding (especially Stata and Python
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applicants who advance discovery in their fields of scholarship using techniques from machine learning and statistics. Applicants may also make research advances in the machine learning and statistical
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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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. Gabriel Vecchi's research group (https://vecchi.princeton.edu) The research will involve modeling and understanding multi-decadal to centennial changes in the statistics of tropical cyclones over the past
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a one-year term position. Responsibilities Responsibilities: Tasks include cleaning datasets, scraping data, and creating summary statistics. Analyze texts and conduct an extensive literature review
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Company: Princeton University Description: Through The center for Statistics and Machine Learning at Princeton University, Princeton Language and Intelligence Initiative is now accepting applications
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datasets (such as those generating by high-throughput sequencing) as well as experience with scripting in bash, python, and/or R is desired. A background in statistics and/or population genetics is also
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platform Familiarity with R Statistical Software and inferential statistics Data analysis and data visualization in R (advanced) Strong written communication (can write an analysis white paper) Ability
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algorithms. 2. Analyze reinforcement learning algorithms. Essential Qualifications Experience with probability/statistics, calculus, machine learning, and reinforcement learning Be advised that you will be