Michigan State University

What you'll study

This PhD combines coursework and independent research to build expertise in mathematical modelling, statistical inference and computational analysis applied to biological systems. Core themes include:

• Mathematical and computational modelling: dynamical systems, stochastic processes, network models, and simulations of cellular, ecological or epidemiological systems.
• Statistical and machine learning methods: modern inferential techniques for high-dimensional data, Bayesian methods, population genetics models, and supervised and unsupervised learning for biological datasets.
• Bioinformatics and data analysis: sequence analysis, comparative genomics, transcriptomics, proteomics, structural bioinformatics and pipelines for high-throughput experiments.
• Interdisciplinary applications: systems biology, evolutionary biology, plant and animal sciences, quantitative genetics, synthetic biology and biomedical informatics.

Programme structure typically includes graduate-level coursework in mathematics, statistics and computing, complemented by biology-focused seminars and domain-specific electives. Students undertake research rotations or early mentorship to identify a dissertation advisor, pass a qualifying or candidacy examination, and then devote the majority of their time to an original research project culminating in a defended dissertation. Regular participation in seminars, journal clubs and departmental teaching or mentoring is expected to develop communication and professional skills.

Entry requirements

Applicants are expected to hold a bachelor’s degree in mathematics, statistics, computer science, engineering, biology or a closely related field. A master’s degree is beneficial but not always required. Typical qualifications include:

• Strong background in calculus, linear algebra, probability and statistics; programming experience in languages such as Python, R, MATLAB or C/C++ is highly recommended.
• Prior coursework or experience in molecular or organismal biology is advantageous for biologically focused projects.
• A record of academic achievement and, where possible, research experience such as undergraduate or master's research, publications, or relevant internships.
• Application materials generally include academic transcripts, a statement of purpose describing research interests, a CV, and letters of recommendation. Standardised test requirements vary and should be checked on the programme website.

Career prospects

Graduates of this programme enter a broad range of research and applied careers. Common paths include:

• Academic careers as postdoctoral researchers and faculty in departments of biology, mathematics, statistics, computer science and interdisciplinary life-science programmes.
• Industry roles in biotechnology, pharmaceutical companies, health-care analytics, and contract research organisations, focusing on bioinformatics, computational biology, data science and modelling.
• Positions in government and non-profit research labs, public-health agencies, and agricultural research organisations applying modelling and genomic analysis to real-world problems.
• Opportunities in technology startups, scientific software development, and consulting where quantitative biology skills are in demand.

The programme emphasises transferable skills — computational reproducibility, statistical rigour, scientific communication and project management — that are valuable across these sectors.

Why study at Michigan State University

Michigan State offers a strong interdisciplinary environment for computational biology, with faculty across departments such as computational mathematics, statistics, computer science, molecular and cellular biology, and plant and animal sciences. Students benefit from collaborations with research centres and institutes that span evolution, genomics, and big-data infrastructure, providing access to high-performance computing, shared core facilities and large biological datasets.

MSU emphasises trainee development through mentorship, teaching opportunities, and professional development workshops. The university’s strengths in agricultural and organismal biology, combined with growing capabilities in biomedical and computational research, create diverse opportunities for experimentally informed computational research and translational projects. Collaborative networks with local and national partners further expand internship and employment prospects for graduates.