Job Title: Postdoctoralfellow
PVN ID: PH-2002-003551
Category: Research

Key Features

Full Time
Depends on Qualifications
Closing Date
Jul 01, 2020 (Or Until Filled)


Job Description

General Description

The CUNY Institute for Implementation Science in Population Health (ISPH) was founded on the notion that substantial improvements in population health can be efficiently achieved through better implementation of existing strategies, policies, and interventions across multiple sectors. With that in mind, we study how to translate and scale-up evidence-based interventions and policies within clinical and community settings in order to improve population health and reduce health disparities. The ISPH is seeking a highly motivated Postdoctoral Fellow who will work on an NIH-funded project with Dr. Hongbin Zhang.  The employee will be an employee of the Research Foundation of CUNY. 



Other Duties

The primary responsibility of this position is to work on an NIH-funded project to develop innovative statistical methods for the analysis of HIV surveillance longitudinal data. Specifically, the successful applicant will work with the PI and other co-investigators on methodology development and software implementation, and application of the methods with the developed software to the analysis of a range of HIV surveillance data.

The postdoctoral fellow will have opportunities to participate in the methods and protocol development, development and deployment of software programs, and dissemination activities of the study (data analysis, manuscript preparation, abstract preparation for conferences, etc.).  This is an excellent opportunity for individuals looking to embark on their own independent research careers in a similar field of study.


Minimum qualifications:

  • doctoral degree in statistics, biostatistics, or related quantitative fields
  • strong programming skills, preferably in R
  • excellent writing skills
  • Interest in complexity (such as missing data) with longitudinal data

Additional desired qualifications:

  • Prior experiences in statistical methodology development
  • A strong background in theoretical development in areas such as mixed effects modeling or computational statistics for high-dimensional data