University of North Carolina at Chapel Hill

What you'll study

The PhD in Biostatistics combines advanced theory, methodological development and applied research. Early years typically emphasise core foundations—probability theory, mathematical statistics, linear models and computational methods—followed by specialised coursework in areas such as survival analysis, longitudinal data analysis, categorical and multivariate methods, causal inference, Bayesian statistics, and high-dimensional data techniques. Students also study topics in clinical trials design and analysis, statistical genetics and genomics, bioinformatics, and machine learning as applied to biomedical data.

Programme structure normally includes a sequence of core courses, elective modules tailored to research interests, passing qualifying or preliminary examinations, completion of a supervised research proposal, and writing and defending a doctoral dissertation. Throughout the PhD students engage in applied collaborations, teaching or research assistantships, departmental seminars and journal clubs, and may take complementary courses in epidemiology, public health, medicine, or computer science to support interdisciplinary projects.

Entry requirements

Applicants should have a strong quantitative foundation typically demonstrated by a bachelor’s or master’s degree in statistics, biostatistics, mathematics, computer science, engineering or a related field. Successful candidates have coursework in calculus, linear algebra, probability and mathematical statistics; prior exposure to applied statistics and programming (R, Python, or equivalent) is strongly recommended. Research experience or applied projects in statistics or biomedical domains is advantageous.

Application materials normally include academic transcripts, a statement of purpose outlining research interests, a curriculum vitae, letters of recommendation, and samples of relevant academic or research work. International applicants whose first language is not English are required to demonstrate English proficiency according to university policy. The programme is competitive and selection emphasises quantitative preparation, research potential, and fit with faculty expertise.

Career prospects

Graduates of the PhD in Biostatistics pursue careers in university faculty positions and academic research, as well as senior scientific roles in industry and government. Common career paths include academic biostatistician or data scientist, methodological researcher, senior statistician in pharmaceutical or biotechnology companies, quantitative scientist in clinical trials and regulatory affairs, and analytic positions in public health agencies, hospitals, research institutes and contract research organisations. Skills developed—methodological innovation, collaborative research, and advanced computing—also translate to leadership roles in health data science, genomics, epidemiology and precision medicine initiatives.

Why study at University of North Carolina at Chapel Hill

The PhD in Biostatistics is housed within a research-intensive environment with strong links between the Gillings School of Global Public Health, the School of Medicine and campus research centres. Students benefit from collaborative opportunities with UNC Health Care, the Lineberger Comprehensive Cancer Center, the Carolina Population Center and other interdisciplinary units, providing access to rich biomedical datasets and translational projects.

Faculty at Chapel Hill have expertise across methodological and applied domains of biostatistics, enabling mentorship in areas such as clinical trials, statistical genetics, causal inference and complex data. The department emphasises professional development through teaching experience, grant-writing support, workshops and a regular seminar series, while university computing resources and collaborative networks support ambitious methodological and applied research programmes. Most PhD students receive financial support through research or teaching assistantships and are integrated into collaborative teams from early in their training.