| Genomics and Cancer |
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Training in Genomics and Cancer at the University of North Carolina at Chapel Hill  This five year program is designed to train predoctoral (PhD) and
postdoctoral students in statistical genomics with the major emphasis in cancer
genomics. The goal is to train biostatisticians in the biology, etiology, and
genetics of cancer, as well as to train them to conduct state-of-the-art
biostatistical methodologic research relevant to the genomics of cancer as well
as in related areas of genomics. The goal is to also produce biostatisticians
who can collaborate with other scientific researchers and oncologists on
research issues related to genomics and cancer. The typical predoctoral trainee
will be a college graduate or masters level graduate with an excellent academic
record appropriate for this training area. The typical postdoctoral trainee
will have highly relevant doctoral training in statistics, biostatistics, or
related areas.
The Department of Biostatistics at UNC is one of the largest in the world, and has highly qualified personnel and the available facilities to provide the most comprehensive predoctoral and postdoctoral training in this research area. Several members of the Carolina Center for Genome Sciences (CCGS) will be deeply involved in all phases of this training program and will play an integral role in this training program. The academic courses for this training program will include all those in theoretical and applied statistics which are the core of a doctoral degree program for a statistician working in the healthsciences, plus relevant courses in genetics, biology, and epidemiology related to cancer research. Biostatistical training in research and consultation will focus on important areas such as computational biology and sequence analysis, DNA microarray analysis, statistical methods in human and quantitative genetics, statistical methods for high dimensional data, multivariate analysis, nonparametric methods, longitudinal data analysis, survival analysis, Bayesian methods, computationally intensive methods, and missing data.
Dr. Joseph G. Ibrahim |
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| Last updated November 17, 2011 |