Select Genetics, Bioinformatics, and Computational Biology from the drop down menu to apply to the program.
Suggested Application Deadlines for all applicants:
Fall Semester: January 1
Spring Semester: September 1
Admission to the Ph.D. Program in Genetics, Bioinformatics, and Computational Biology will follow the general guidelines set forth by the university and will include:
A bachelor’s degree from an accredited college or university or its equivalent.
A minimum undergraduate GPA of 3.0 on a 4.0 scale.
- NO GRE REQUIRED
- For applicants whose native language is not English, satisfactory scores from a standardized test commonly used and deemed appropriate for evaluation of English Language Proficiency, such as the TOEFL (a minimum score of 600 will be expected) or the IELTS (a minimum score of band 7). IBT accepted also
- Three letters of recommendation.
- A written statement of intent/personal statement for pursuing graduate studies.
The goal of the program is to develop PhD candidates' skills as they perform original research in the areas of genetics, bioinformatics, and computational biology. This enables our graduates to pursue careers in academia, government, or the private sector.
The mission of the Genetics, Bioinformatics and Computational Biology Program is to combine discipline-specific and cross-disciplinary course work with a multidisciplinary research environment that is supported by program faculty and distinguished by a high level of collaboration.
The scientific and training focus of the program involves three significant interdependent areas in the post-genomic era:
- Experimental approaches and technologies for addressing complex biological questions,
- Methods for collection, management and analysis of large data sets,
- Data-based modeling of biological systems.
The program will stress skills that include:
- Design and implementation of experiments that generate large biological data sets, for the purpose of analyzing complex biological systems.
- Development and application of methods to collect, store, organize, and visualize large biological data sets.
- Development and application of statistical, mathematical and computational tools for the interpretation of large biological data sets.
- Development and application of data-based mathematical modeling and simulation techniques for biological systems.