Michigan Technological University

What you'll study

The PhD programme combines advanced coursework, computational training and original research. Core topics typically include probability theory, asymptotic and finite-sample statistical inference, linear and generalized linear models, multivariate analysis and stochastic processes. Biostatistics-specific topics commonly offered or pursued as electives include survival analysis, longitudinal data analysis, clinical trials methodology, causal inference, statistical genetics and genomics, high-dimensional data analysis, Bayesian methods and computational statistics (including MCMC and other simulation-based techniques).

• Advanced theoretical courses in probability and statistical inference
• Regression, linear models and design of experiments
• Survival and longitudinal models for time-to-event and repeated measures data
• Bayesian statistics and computational methods
• High-dimensional data analysis, machine learning and statistical genomics
• Practical training in statistical software and high-performance computing
• Doctoral seminars, teaching practicum and ethics in research
• Independent research leading to a doctoral dissertation

Students normally complete a combination of required and elective coursework, pass qualifying examinations or comprehensive assessments to achieve candidacy, and then devote the majority of their programme to original research under the supervision of a faculty advisor. Many students participate in collaborative applied projects with researchers in health sciences, biology and engineering to gain practical experience handling biomedical data.

Entry requirements

Applicants are expected to hold a relevant master’s degree (for example, statistics, biostatistics, mathematics, or a closely related quantitative discipline) or an exceptional bachelor’s degree with substantial coursework in probability and statistics. A strong background in calculus, linear algebra, mathematical statistics and programming is required. Successful applicants typically demonstrate:

• Transcripts showing solid quantitative performance in undergraduate and any graduate coursework
• A statement of purpose describing research interests and fit with departmental faculty
• Letters of recommendation that speak to research potential and quantitative ability
• Evidence of programming or computational experience (for example, R, Python, C/C++ or MATLAB)
• For international applicants, proof of English language proficiency where required by university policy

The department assesses research potential, prior coursework and the match between applicant interests and faculty expertise. Financial support in the form of teaching or research assistantships is often available for admitted students; applicants are encouraged to highlight relevant research experience and potential faculty mentors in their materials.

Career prospects

Graduates of the PhD programme pursue careers in a wide range of sectors that require advanced statistical and biostatistical skills. Typical career paths include academic positions in statistics or biostatistics, research scientist roles in pharmaceutical and biotechnology companies, positions in contract research organisations (CROs) supporting clinical trials, and applied research roles in public-health agencies and governmental research laboratories.

• University faculty and academic researchers in biostatistics or related fields
• Biostatistician or statistical scientist in pharmaceutical/biotech firms
• Design and analysis of clinical trials at CROs or medical-device companies
• Data scientist or quantitative researcher in public-health organisations and government agencies
• Industry roles in genomics, epidemiology, health informatics and precision medicine

PhD graduates are prepared to lead methodological research, design and analyse complex biomedical studies, and develop computational tools for high-dimensional and longitudinal biological data.

Why study at Michigan Technological University

Michigan Technological University’s Department of Mathematical Sciences provides a close-knit research environment with strengths in both theory and computation. The programme emphasises interdisciplinary collaboration, enabling PhD students to work with faculty across biological sciences, health-related research and engineering on applied biostatistical problems. Students benefit from access to high-performance computing resources, opportunities for funded research and teaching assistantships, and a departmental culture that supports hands-on applied projects alongside rigorous methodological training.

Located in a region where collaborative projects with regional health and environmental researchers are common, the department encourages students to develop skills that are immediately transferable to applied research settings while also supporting fundamental methodological contributions to the field of biostatistics.