Indiana University Bloomington

What you'll study

The programme combines advanced coursework in probability and theoretical statistics with specialised training in biostatistical methods and extensive research leading to a dissertation. Early years are typically devoted to core courses and preparatory modules, followed by advanced elective options and sustained dissertation research under the supervision of a faculty advisor.

• Core coursework: measure-theoretic probability, mathematical statistics, asymptotic theory, and linear models.
• Biostatistics-focused modules: survival analysis, longitudinal and correlated data methods, generalized linear and mixed models, causal inference for observational studies, and clinical trials methodology.
• Computational and data-analytic skills: statistical computing and programming (R, Python), Monte Carlo methods, Bayesian computation, and high-dimensional data analysis.
• Interdisciplinary and applied training: practicum-style projects, collaborative courses with public health, epidemiology and biomedical researchers, and opportunities for internships or placements with clinical or public-health partners.
• Research and assessment: qualifying examinations or coursework milestones, candidacy requirements, and a doctoral dissertation involving original methodological or applied biostatistical research.

Programme structure

Students normally complete a sequence of core and elective courses in the first two to three years while preparing for qualifying examinations. After passing qualifying milestones and completing required coursework, students focus on dissertation research, typically supervised by a biostatistics or statistics faculty member. Teaching assistantships and research assistantships are common components of the training, providing experience in instruction and funded research.

Entry requirements

Applicants should hold a strong undergraduate or master’s degree in statistics, mathematics, biostatistics, computer science, or a closely related quantitative discipline. Successful candidates typically demonstrate:

• a strong foundation in calculus, linear algebra, probability and mathematical statistics;
• experience with statistical computing and programming;
• evidence of research potential, which may include a thesis, publications, research assistant experience, or strong letters of recommendation;
• a clear statement of research interests that aligns with faculty expertise in biostatistics and related areas.

Applications usually require official academic transcripts, a curriculum vitae, letters of recommendation, and a personal statement. Applicants whose first language is not English will need to demonstrate English proficiency according to the university’s requirements. Prospective students with a master’s degree in a related field may be admitted with advanced standing depending on prior coursework.

Career prospects

Graduates of a PhD in Statistics with biostatistics emphasis pursue careers across academia, industry, and the public sector. Typical pathways include:

• academic positions as faculty or postdoctoral researchers in statistics, biostatistics, epidemiology or public health departments;
• research scientist or statistician roles in pharmaceutical and biotechnology companies, designing and analysing clinical trials and translational studies;
• analyst and modelling positions in government health agencies, public-health institutes and non-profit research organisations;
• data-science and quantitative research roles in technology and healthcare analytics companies, contract research organisations, and consulting firms.

Graduates are often sought for their ability to develop new methodology, lead collaborative biomedical research, and communicate complex quantitative results to multidisciplinary teams.

Why study at Indiana University Bloomington

Indiana University Bloomington offers a strong doctoral environment in statistics with access to faculty working on both methodological and applied biostatistics problems. The department provides rigorous theoretical training alongside practical collaboration opportunities with public-health and medical researchers across the university, enabling students to work on real-world biomedical data and clinical problems.

Students benefit from a research-oriented culture, access to statistical computing resources, opportunities for teaching and funded research assistantships, and an academic community that supports interdisciplinary projects. The programme’s location within a large public research university facilitates partnerships with public-health experts, medical researchers and other quantitative scientists, which strengthens training and job-readiness for a range of biostatistics careers.