113 parallel-computing-numerical-methods Fellowship research jobs at Harvard University
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, statistical analysis of data, mathematical modeling, and communication of results, with the aim of guiding policy for climate change adaptation. Experience with theoretical and experimental methods
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. Research areas include Representation Learning, Machine learning and Optimization on graphs and manifolds, as well as applications of geometric methods in the Sciences. This is a one-year position with
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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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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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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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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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)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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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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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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will involve investigating the physiological and functional variation of plants using spectroscopy and will include greenhouse work, field work, plant phenotyping, computational analyses of hyperspectral