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Pre-Doctoral Fellowship - Cambridge, United States - Harvard University
Description
DetailsTitle Pre-Doctoral Fellowship (Prof Gabaix)
School Faculty of Arts and Sciences
Department/Area
Position Description
Professor Xavier Gabaix seeks a highly skilled and motivated individual to work as a full-time predoctoral fellow for a period of at least one year.
The preferred start date is August 1, 2024 although earlier or later start dates may be feasible, including starting this academic year.
We're looking for two types of predoctoral fellows:
Ideally, you would have a Masters in computer science, but a strong undergraduate training in computer science is a fine substitute.
Knowledge of economics/finance is optional.Still, the position is particularly instructive if you want to pursue a PhD later in economics or finance, or perhaps in computer science.
For the style of research involved in the "Machine learning" track, see the paper "Asset Embeddings \_id= " at \_id= The research is joint with Ralph Koijen, Robert Richmond and Moto Yogo from U.
The fellow will also be an active participant in the Harvard University research community.
The fellowship includes opportunities to develop a research agenda in related topics, to attend seminars, and to take some courses.
The position is the ideal training ground for a recent graduate who is interested in pursuing a PhD in Economics.
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. Application review will continue after this date until the position is filled. Application review will continue after this date until the position is filled.
Basic Qualifications
Special Instructions
Contact Information
Shree Manari
Contact Email
Equal Opportunity Employer
We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.
Maximum Number of References Allowed
Keywords
Supplemental Questions