141 parallel-computing-numerical-methods research jobs at Harvard University in United States
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(strongly preferred) Interest in applying to a quantitative PhD program and pursuing an academic career (strongly preferred) We prefer research assistants who want to become professional economists, but we’re
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improve their financial outcomes. The projects would be developed and completed in collaboration with CBA. Primary methods would include analysis of large-sample behavioral data, surveys, and lab and field
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with bioinformatic pipelines and approaches for working with methylation data, or a willingness/ability to learn these methods. The appointment is for one year with possibility of renewal based
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or computational research, the successful candidate will be expected to apply for fellowship funding, contribute to the writing of grants and manuscripts, participate in teaching and mentoring of lab members as
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Details Title Postdoctoral Fellowship in Power and AI Systems School Harvard John A. Paulson School of Engineering and Applied Sciences Department/Area Computer Science/ Electrical Engineering
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rolling basis. The position will remain open until filled. Basic Qualifications A Ph.D. in Mathematics, Applied Mathematics, Computer Science, or a related field, by the start of the appointment. Additional
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January 11, for awards is February 1, and for the internship program is February 15. Undergraduate Students Graduate Students and Advanced Undergraduates Post-doctoral Fellows Early-Career Scholars (from
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)colonial Indigenous settings in the USA. Responsibilities Under the supervision of Prof. Joseph Gone, Faculty Director of the Harvard University Native American Program, and in collaboration with regional
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and Sciences (FAS) is the historic heart of Harvard University. It is the home of Harvard’s undergraduate program (Harvard College, founded in 1636) as well as all of Harvard’s Ph.D. programs
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to precisely track brain and cognitive change over short intervals. The program of research seeks to understand individual differences in aging trajectories and to develop approaches to predict and monitor