University of Michigan

What you'll study

This programme combines coursework in mathematics, statistics, computer science and molecular biology to give you the practical and theoretical tools used in modern computational biology. Core topics typically include statistical genomics and population genetics, machine learning for biological data, algorithms for sequence and structural analysis, systems and network biology, mathematical models of biological processes (deterministic and stochastic), and high-performance computing and data management for large-scale experiments.

Programme structure normally mixes advanced core modules with electives drawn from departments such as Biostatistics, Computer Science and Engineering, Molecular, Cellular, and Developmental Biology, and Mathematics. Students usually complete a combination of:

• Core modules in probability, statistics and computational methods for biological data
• Electives in topics such as deep learning for genomics, structural bioinformatics, evolutionary modelling, and single-cell data analysis
• Practical laboratory or computing projects emphasising reproducible workflows, data pipelines and software tools (R, Python, C/C++, cloud/HPC environments)
• A capstone project or research thesis supervised by faculty, often involving collaboration with research groups or interdisciplinary centres

Teaching formats include lectures, hands-on programming and data-analysis labs, journal clubs and seminars with faculty and invited speakers. Assessment commonly combines coursework, project reports, presentations and an independent research component.

Entry requirements

Applicants are expected to hold a bachelor’s degree in a quantitative or biological discipline such as mathematics, statistics, computer science, engineering, physics, or biology. Typical preparation includes coursework in calculus, linear algebra, probability and statistics, programming (Python, R or similar), and an introductory course in molecular or cellular biology or genetics.

Selection is based on academic transcripts, a personal statement describing research interests and relevant experience, and letters of recommendation. Relevant laboratory or computational research experience, demonstrated programming skills, and coursework in statistical methods are advantages. Standardised test requirements vary by programme and may be optional; consult the admissions office for current policy. International applicants will need to demonstrate English language proficiency where required.

Career prospects

Graduates are prepared for a wide range of careers that bridge biology and quantitative sciences. Common roles include:

• Bioinformatics analyst or computational biologist in academia, hospitals, biotech and pharmaceutical companies
• Data scientist and machine learning engineer working on biomedical datasets
• Research scientist in genomics, proteomics, systems biology or structural biology groups
• Software developer specialising in scientific computing, data pipelines or visualization tools
• Public health analyst or biostatistician applying computational methods to population and clinical data

Many graduates continue to doctoral study in computational biology, biostatistics or related fields. The University of Michigan’s connections to research hospitals and industry partners also support internship and job opportunities in the broader life‑sciences and technology sectors.

Why study at University of Michigan

University of Michigan offers an interdisciplinary environment with faculty across departments who work at the intersection of computation and biology. Students gain access to specialised research facilities, high-performance computing resources and core sequencing and imaging platforms, and benefit from collaborative centres that bring together engineers, statisticians and biomedical researchers.

The campus and Ann Arbor region provide a vibrant academic community and links to a diverse biotechnology and health‑technology ecosystem. Support services for career development, entrepreneurship and industry engagement help students translate technical training into internships, collaborative projects and employment. The programme’s integration of rigorous quantitative training with practical research experience makes it suitable for students seeking either immediate employment in industry or further research training at the doctoral level.