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competitive, typically over USD 55,000/year. Alumni of this RA-ship have been admitted to top PhD program, such at Harvard, MIT, Princeton, Stanford. *The position is fully paid, and a visa can be arranged
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goal of this project is to develop machine learning techniques to improve climate change simulations. M²LInES is composed of more than 30 scientists at NYU, Columbia, MIT, Princeton, GFDL, NCAR and CNRS
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O'Gorman (MIT), and Pierre Gentine (Columbia University). The successful applicant is also expected to actively collaborate with other M²LInES team members, with scientists and engineers at NCAR, and with
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experience in the analysis single-cell and other types of high-throughput data (e.g., RNA-seq, ATAC-seq, ChIP-seq, CUT&TAG, and chromatin interaction). You should also have a PhD in a relevant subject, a sound
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with CHAMPS leadership and scientific oversight team, develop and implement priority nutrition-related research questions for CHAMPS. Monitor and evaluate nutrition-related data collected in CHAMPS MITS
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proximity to the economics departments of Harvard, MIT, Stanford, and University of Chicago. Schaeffer Center experts are also frequent advisors on national policy. In 2023, Schaeffer-affiliated faculty
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for candidates who are interested in pursuing a PhD in business or economics afterwards (many of our past RAs were admitted into top PhD programs including Harvard, MIT, Chicago, Berkeley, Toronto, Columbia, and
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to effectively engage students from a range of backgrounds and experiences. Preferred Education and Experience: a PhD in Human Resource Management or Industrial/Organizational Psychology is preferred. Further
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at Cornell University, but the project will include close collaboration with colleagues at Penn State and MIT, as well as a machine learning-focused geothermal start-up (Strabo Analytics). The entire project
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database dedicated to economics. This places the Schaeffer Center in proximity to the economics departments of Harvard, MIT, Stanford, and University of Chicago. Schaeffer Center experts are also frequent