With rapid population aging, Alzheimer's Disease and Related Dementias (ADRD) has become one of the most expensive diseases and a leading cause of death in America and across the globe. Black Americans are twice as likely to have ADRD as Whites, while the prevalence for Hispanics is about 50 percent higher than that of Whites. The increasing share of older population who are Blacks or Hispanics will considerably increase the economic and social burden associated with ADRD in America. The root of cognitive aging and eventual ADRD can be traced back to early life. In particular, Blacks and Hispanics often experience more disadvantaged early life circumstances than Whites, which may bear long-lasting consequences. However, there is lack of understanding of the role that early-life circumstances may play in ADRD and its racial/ethnic disparities. Early-life circumstances considered in this project will include but not limited to place of birth, family resources, access to health care, quality of education, racial segregation, and childhood traumas.
As part of this project, the pre-doc will be involved in all parts of the research process and have the opportunity to develop a broad array of research-related skills. They will work with nationally representative longitudinal surveys linked with administrative data, work on applying appropriate causal inference and machine learning methods, contribute to the drafting of manuscripts that target high impact journals, and join us in communicating findings at major academic events. Ample training opportunities will be available across the Yale campus.
The pre-doc hired for this position would work closely with Professors Xi Chen, Thomas M. Gill (Director, Yale Program on Aging), Heather Allore (Leader, Data Management and Statistics Core of the Yale Alzheimer's Disease Research Center), and their coauthors.
For more information about Professor Xi Chen, visit
REQUISITE SKILLS AND QUALIFICATIONS:
We are looking for candidates who have a strong interest in pursuing impactful research, enjoy working with complex data sets, using causal methods, working with a research team, and being detail-oriented and enthusiastic about problem-solving. Applicants should be completing or have completed a bachelor’s or master’s degree, have previous research experience and excellent programming skills. A major in economics is not required, but an interest in economics as well as coursework in economics and econometrics is. Methodological interests in health economics, economics of aging, econometrics, machine learning, or statistics are highly desirable. Prior coding experience in a statistical language (especially Stata or R) is essential. We prefer candidates who can commit to 2 years of work.
SPECIAL APPLICATION INSTRUCTIONS:
To apply, please email email@example.com a pdf document titled “lastname_firstname” and the following materials:
- A cover letter describing your interest, dates available, your prior research experience, your familiarity with programing languages, and the names, email addresses and phone numbers for 2-3 references;
- A current CV;
- A transcript;
- A writing sample;
- A sample of your coding.