This is a renewal application of the Interdisciplinary Training Program in Statistical Genetics/Genomics and Computational Biology at the Harvard School of Public Health (HSPH). Trainees will be pre-doctoral students at HSPH in the Departments of Biostatistics and Epidemiology, which will jointly administer the grant. The program proposes support for seven pre-doctoral students. The goal of the program is to train the next generation of quantitative genomic scientists to have a strong understanding of, and commitment to, cutting- edge methodological and collaborative research in statistical genetics/genomics and bioinformatics/computational biology with applications in genetic epidemiology, molecular biology and genomic medicine. We are committed to train trainees to become future quantitative leaders to develop and apply advanced, scalable statistical and computational methods to manage, analyze, integrate, and interpret massive genetic and genomic data in basic science, epidemiological and clinical studies, to promote interdisciplinary research, and to effectively communicate and collaborate with subject-matter scientists in genetic and genomic research. In this renewal, we will expand the scope of the training program to enhance quantitative training in big `omics data science and reproducible research. The training program involves active participation by accomplished and experienced multidisciplinary faculty members, including biostatisticians, bioinformaticians and computational biologists, genetic epidemiologists, and molecular biologists. It combines elements of training in coursework, lab rotations in both wet labs in biological science and dry labs in biostatistics, computational biology, and genetic epidemiology, directed methodological and collaborative research, and rich career development opportunities in a stimulating and nurturing interdisciplinary environment, that will prepare graduates to become leading quantitative genomic scientists.
8. Groundbreaking research and discovery in the life sciences in the 21st century are more interdisciplinary than ever. To expedite scientific advances in the 'omics' era, it is critical to train the next generation of quantitative health science students wh are strong in biostatistics, computational biology, molecular biology and genetic epidemiology, and who have enough basic knowledge that they can easily communicate and work with colleagues who have complementary areas of expertise.
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