Department of Biostatistics

Doctoral Program

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 overall goal of the Ph.D. program in Biostatistics is to prepare individuals to become leaders in the field through the achievement of six competencies:

  1. Discuss and apply a foundational knowledge base in probability, biostatistical theory, and computation to conduct collaborative and methodologic research.
  2. Synthesize established and recently-developed knowledge in probability, biostatistical theory, computation methods, and in public health, clinical and biological sciences.
  3. Teach statistical theory or methodology at multiple levels.
  4. Design and develop grants or proposals towards obtaining funding/resources for future research activities.
  5. Collaborate and communicate effectively with research scientists in related disciplines.
  6. Develop, implement and disseminate novel biostatistical or bioinformatics methodology to address outstanding questions in public health, clinical and biological science.

ELIGIBILITY REQUIREMENTS

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, R, Stata, or Python
  • 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.

FUNDING

We provide full financial support (stipend/salary, tuition and health insurance) to all doctoral students in good standing for 5 years. This support comes from a variety of sources, including:

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.

Harvard University does not discriminate against applicants or students on the basis of race, color, national origin, ancestry or any other protected classification.