PhD in Biostatistics
The PhD program is designed for those who have demonstrated both interest and ability in scholarly research. The department’s program is designed to prepare students for careers in the theory and practice of biostatistics and bioinformatics, and includes training in the development of methodology, consulting, teaching, and collaboration on a broad spectrum of problems related to human health, genomics, and basic biology.
Download the Biostatistics Doctoral Student Handbook for more information on the doctoral program, and download the Biostatistics Master’s Student Handbook for more information on the Masters’ programs.
The Ph.D. program in Biostatistics prepares students in the following five competencies:
- Applying innovative probabilistic and statistical theory and computing methods to the development of new biostatistical or bioinformatics methodology, publishing of original methodological research, and the solution of public health problems
- Providing scientific and biostatistical or bioinformatics leadership in the design, conduct, and analysis of collaborative research studies in medicine and public health
- Applying modern statistical and computational methods to effectively analyze complex medical and public health data, including the development of new software for non-standard problems and simulation methods
- Collaborating and communicating effectively with research scientists in related disciplines
- Teaching biostatistics or bioinformatics effectively to health professionals, research scientists, and graduate students
All candidates for admission to the Ph.D. programs must have:
- Successfully completed calculus through multivariable integration and one semester of linear algebra
- Knowledge of a programming language
All candidates for admission to the Ph.D. programs are encouraged to have:
- Completed courses in: probability, statistics, advanced calculus or real analysis, and numerical analysis
- Practical knowledge of a statistical computing package such as SAS, Splus, R, Stata, or SPSS
- Have completed courses in biology, computational biology, and genetics, if interested in bioinformatics
- Knowledge of a scripting language such as Python or Perl and some familiarity with relational databases, if interested in bioinformatics
On rare occasions the Department will admit students to our programs without this level of preparation with the understanding that the student will promptly make up any deficiencies, usually by taking additional courses prior to entering the program.
We provide full financial support (tuition, fees, and stipend) to all doctoral students in good standing for 4 to 5 years. This support comes from a variety of sources, including:
- NIH training and research grants
- Teaching fellowships
- Competitive Harvard Chan School & GSAS scholarships
We also encourage doctoral students to apply for outside funding during their senior year of undergraduate study, such as NSF and NDSEG fellowships, which often award stipends larger than those provided by the Department. Foreign applicants may be eligible for funding from Harvard-related sources based in their home country as well.