University of Virginia

What you'll study

The programme combines advanced coursework in probability and statistical theory with specialised biostatistical methods and intensive research. Early coursework typically covers core subjects such as probability theory, mathematical statistics, statistical inference, and linear models. Biostatistics-focused modules commonly include survival analysis, longitudinal and repeated‑measures methods, categorical data analysis, clinical-trials design and analysis, causal inference, and modern methods for high-dimensional and functional data.

Students also take courses in statistical computing and data science (including programming in R, Python and reproducible workflows), Bayesian statistics, and elective topics that reflect faculty research strengths. Seminar series and journal clubs expose students to current applied problems in biomedical research. Training emphasises both methodological development and substantive collaboration with biomedical investigators.

The doctoral process typically involves a period of structured coursework, passing qualifying examinations (written and/or oral), and progressing to independent research under the supervision of a faculty advisor. The culmination is an original research dissertation. Alongside research, PhD students commonly fulfil teaching or research assistantship responsibilities and participate in interdisciplinary collaborations with the School of Medicine, public‑health groups and clinical investigators.

Entry requirements

• Academic background: A strong quantitative degree (master's or bachelor's) in statistics, mathematics, biostatistics, computer science, engineering, economics or a closely related field. Prior coursework in calculus, linear algebra, probability, and mathematical statistics is expected.
• Research potential: Evidence of research ability or relevant applied experience is highly desirable. This can include a master's thesis, research projects, publications, or substantive applied work in biomedical settings.
• Application materials: A detailed statement of purpose outlining research interests and fit with departmental faculty; full academic transcripts; a current CV; and three letters of recommendation from academics or employers who can speak to quantitative and research abilities.
• Technical skills: Demonstrable programming experience (for example in R, Python, or SAS) and familiarity with data analysis are beneficial.
• Standardised tests and other requirements: Specific test policies (such as GRE or English language tests) and minimum GPA expectations are set by the department and may vary—applicants should consult the programme website for current guidance.

Career prospects

Graduates of the PhD in Statistics with biostatistics emphasis pursue a broad range of careers. Common paths include tenure‑track academic positions in statistics or biostatistics departments, and research scientist roles in university research centres and hospitals. Many alumni work in the pharmaceutical and biotechnology industries as biostatisticians designing and analysing clinical trials, or in contract research organisations and regulatory agencies where expertise in clinical design and inference is essential.

Other opportunities include positions in public‑health agencies, disease surveillance and epidemiology units, health‑data analytics teams, and technology firms that focus on biomedical data. The programme prepares graduates for leadership roles that require both methodological innovation and collaborative applied skills.

Why study at University of Virginia

• Interdisciplinary collaboration: UVA offers strong links between the statistics faculty and clinical, public‑health and biomedical researchers, providing rich opportunities for applied projects and co‑supervised research.
• Research strengths: Faculty research covers a wide range of biostatistical areas—survival and longitudinal methods, causal inference, clinical‑trial methodology, Bayesian approaches, and high‑dimensional data—allowing students to find close mentorship in their chosen specialism.
• Resources and training environment: Students benefit from departmental seminars, workshops, computing resources, and access to patient‑centred datasets through affiliated medical and research units, supporting both methodological and translational research.
• Professional development: The department supports teaching experience, grant-writing practice, and career preparation for academic and non‑academic roles, aided by a campus environment that encourages collaboration across disciplines.