University of Michigan

What you'll study

The programme combines core statistical theory with advanced methods tailored to biomedical applications. Early coursework typically covers probability theory, mathematical statistics, linear models, and computational statistics, followed by specialised topics such as survival analysis, longitudinal and repeated‑measures methods, causal inference, Bayesian modelling, high‑dimensional data analysis, statistical genetics and functional data analysis. Courses in clinical trial design, diagnostic test evaluation and missing data methods are common for students focusing on medical applications.

Training emphasises both methodological development and applied experience: students work on real biomedical datasets, learn modern computational tools for reproducible research, and gain expertise in programming languages and software used in biostatistics. The programme structure normally includes a sequence of required and elective courses, a qualifying or preliminary examination to confirm readiness for research, participation in research seminars and journal clubs, supervised pedagogy or teaching assignments, and a sustained original research project that culminates in a doctoral dissertation.

Entry requirements

Applicants are expected to have a strong quantitative background. Typical preparation includes an undergraduate or master’s degree in statistics, biostatistics, mathematics, applied mathematics, computer science or a related quantitative field, with solid coursework in calculus, linear algebra, probability and mathematical statistics. Practical experience with data analysis and programming is advantageous, as is prior research experience or a master’s thesis.

Admission materials generally include academic transcripts, a statement of purpose describing research interests, a curriculum vitae, and multiple letters of recommendation from academic or professional referees who can speak to quantitative ability and research potential. International applicants must demonstrate English proficiency. Admissions committees look for evidence of intellectual maturity, motivation for independent research, and fit with faculty interests; prospective applicants are encouraged to review faculty profiles and potential supervisors' research areas before applying.

Career prospects

Graduates pursue diverse careers across academia, industry and the public sector. Common pathways include tenure‑track faculty positions in statistics, biostatistics or epidemiology; methodological and applied research roles in pharmaceutical and biotechnology companies; positions in clinical trials and regulatory science; roles in public‑health agencies, national research institutes and hospitals; and data‑science or analytics positions in health‑tech, insurance and consulting firms.

With training in both theory and application, PhD graduates are prepared to lead interdisciplinary teams, design and analyse complex studies, develop new statistical methodology, and translate quantitative results into health policy and practice.

Why study at University of Michigan

The University of Michigan offers a highly interdisciplinary environment that connects statistics and biostatistics with medicine, public health, genomics and computational data science. Faculty members conduct collaborative research across clinical departments and research institutes, providing students access to a broad range of applied problems and large biomedical datasets.

Resources at the university support advanced computational work, cross‑departmental seminars and training programmes, and opportunities for collaborative grants and internships. The breadth of clinical, public‑health and life‑science partners means students can pursue translational research projects with real‑world impact while receiving rigorous methodological mentorship.

Prospective students benefit from a balance of strong theoretical training and extensive applied exposure, preparing graduates to become leaders in biostatistical research and practice.