Internships and Fellowships


2019 Internships

Internship Opportunities Listing Form for Organizations


Biometric-Genetic Analysis of Cardiovascular Disease Postdoctoral Fellowship
This NIH-funded program provides predoctoral and postdoctoral fellowships to U.S. citizens and those lawfully admitted to the United States for permanent residence, specifically in the areas of statistical genetic analysis and genetic epidemiology with applications to the study of risk factors for cardiovascular and pulmonary disease. Predoctoral training is for the PhD degree in epidemiology and biostatistics through the division of genetic and molecular epidemiology at Case Western Reserve University.

Computational Genomic Epidemiology of Cancer Postdoctoral Fellowship
This program provides postdoctoral training in the computational genomic epidemiology of cancer. It defines a novel, transdisciplinary area of training at the intersection of cancer research, genetics, epidemiology, biostatistics, and computer science.

Survey Data Analysis and Quality Assurance Service Fellowship Program
This fellowship at the National Center for Health Statistics (NCHS) of the Centers for Disease Control and Prevention provides experience in preparation, analysis, and dissemination of National Health Interview Survey data. The survey collects health and demographic data, including health conditions, health behaviors, health care utilization, health insurance coverage, and socioeconomic topics. This fellowship is open to all citizens of the United States and legal permanent residents with a work authorization who have completed a master’s degree or higher. View other NCHS fellowships.

Postdoctoral Position in Biostatistics (1 year, renewable once) Biostatistics Team, Bordeaux, France
A postdoctoral position in biostatistics is available in the biostatistics team within the INSERM Research Center “Epidemiology and Biostatistics” of the Bordeaux School of Public Health. The theme of the postdoctoral research is the development of dynamic biostatistical models for oncological applications. The first aim of this project is to develop a new model for analyzing simultaneously repeated measures of a biomarker and multiple events, both in the context of multiple failure types and recurrent events and a terminal event. A specific focus will be on dynamic predictive tools that can be derived from this joint model and new methods to evaluate its predictive accuracy. These developments will be applied in the context of prostate cancer progression after radiation therapy, when repeated measures of PSA are collected with multiple events such as clinical recurrence, death, or possible initiation of a new treatment. Questions about this position should be addressed to Virginie Rondeau.