The online application opened on September 1, 2021 and can be found at the Duke University Graduate School application website.
Please note that the only method of applying to our Ph.D. program in Biostatistics is through the Duke University Graduate School application website: materials emailed or mailed to individual faculty members will not be reviewed by our Admissions Committee.
The 2022-2023 academic year deadline is December 1, 2021. The Department matriculates Ph.D. students in the fall only.
When submitting an application, candidates are required to have the following:
- A completed on-line Graduate School application
- Transcripts from all undergraduate and graduate institutions where you earned (or will earn) a degree, studied for one semester or more, or took classes that relate to your current application for graduate study
- Three letters of recommendation
- Statement of purpose
- Graduate Record Examination (GRE) scores are not required for students applying to the PhD in Biostatistics Program for Fall 2021 matriculation. Additional information about the Duke University Graduate School application may be found at the Graduate School application website.
- Test of English as a Foreign Language (TOEFL) score (If applicable) or International English Language Testing System (IELTS) score (If applicable) or the Duolingo English Test score
Additional information about the Duke University Graduate School application may be found at the Graduate School application website.
The application fee is $95.
The Graduate School provides a limited number of application fee waivers. All fee waiver requests are reviewed by the Assistant Dean for Graduate Student Development in the Office of Graduate Student Affairs. The fee waiver request deadlines are November 15 (for Ph.D. applicants). Applicant may request a fee waiver prior to or by the deadline. Requests may be emailed directly to Assistant Dean Alan Kendrick at email@example.com. Fee waiver requests received after the deadlines will not be reviewed.
Some of the traits associated with success in our program are problem definition, problem solving, quantitative thinking, critical thinking, communication, intellectual curiosity, and dedication. Regarding training, a mathematical background is particularly important.
Students will need proficiency in both single-variate and multi-variate calculus and linear algebra. Specifically, incoming students should have a working knowledge of:
- Optimization of functions
- Inverse functions
- Sequences, series and convergence, Taylor series
- Convergence of sequences of functions (pointwise, uniform, in measure)
- Differentiation and integration of single- and multi-variate functions
- Fubini's theorem
- Fundamental theorem of calculus
- Matrix algebra and vectors
- Vector spaces, metric spaces, Hilbert spaces
- Linear operators
- Spectral theorem and diagonalization of matrices
- Inner products, quadratic forms and projections
A course in real analysis is strongly recommended. As curricula can vary, please note that a suitable course would cover the following (in addition to the usual topics in derivatives and integrals):
- Real and complex number systems
- Basic point-set topology (compactness, continuity, connectedness)
- Metric spaces
- Numerical sequences, convergence, Cauchy sequences
- Sequences and series of functions
For an example text, please see Rudin's “Principles of Mathematical Analysis” (known as 'Baby Rudin').