Professors Davila and Huo are jointly recruiting one Fellow to work on the following projects:
The successful applicant will work with Professors Davila and Huo on projects of theirs and their coauthors. Most of the projects will involve quantitative work similar to the work described in http://www.quantecon.org/. Professor Davila’s recent research has included mostly normative work on financial frictions, household finance, financial trading, and financial intermediation. Professor Huo’s recent research has included work on information frictions, expectation formation, and quantitative heterogeneous-agents models. For more information, visit http://www.eduardodavila.com and http://zhenhuo.weebly.com. While a lot of the existing predoctoral research opportunities are geared towards the empirical analysis of datasets, we are looking for more theoretically oriented applicants who want to combine exposure to theoretical modeling with learning of computational methods in Economics.
The ideal candidate will
- have a strong analytic background;
- have strong computer and data skills, including programming in Matlab, Stata, Python, Julia, R, or similar;
- be able to work independently to solve problems, and
- have a long-term interest in pursuing research in Economics.
Background in Economics is a plus, but not necessary. We welcome candidates with strong technical backgrounds who are looking for more exposure to Economics.
This position is ideal for someone who has a long-term interest in Economics research and is planning to purse graduate studies in Economics. We will be hiring on a renewable one-year contract. Our preference is for candidates who can work for two years.
1. A short cover letter (no more than one page) describing:
(a) Your interest in the position and career goals
(b) The date you are able to start work
(c) Your familiarity with Julia, Python, Matlab, R, Fortran, or comparable languages
(d) Your prior experience as a research assistant and with independent research (e.g., a senior thesis)
2. A current CV
3. A transcript