University of Michigan

What you'll study

This doctoral programme emphasises rigorous training in quantitative methods and their application to biological data and systems. Core topics typically include statistical genomics and population genetics, machine learning for biological data, systems and network biology, mathematical modelling of biological processes, computational structural biology, algorithm design for sequence and -omics analysis, and high-performance computing for large-scale datasets.

Students follow an individualised curriculum combining graduate coursework and intensive research. Early years commonly include foundational courses in probability and statistics, computational methods, advanced topics in molecular and cellular biology, and specialised seminars. Students usually complete research rotations with faculty across departments, pass a qualifying examination to advance to candidacy, and then focus on an independent dissertation project that results in peer-reviewed publications.

• Advanced statistical modelling and inference for genomic data
• Machine learning and data mining in bioinformatics
• Mathematical and computational modelling of cellular and physiological systems
• Algorithmic and software development for sequence and structural analysis
• High-performance and parallel computing for large biological datasets
• Ethics, reproducibility, and best practice in computational research

Entry requirements

Applicants are expected to hold a bachelor’s degree or master’s degree in a relevant field such as mathematics, statistics, computer science, engineering, physics, or a biological science. Strong applicants demonstrate substantial quantitative preparation (calculus, linear algebra, probability/statistics), programming experience (Python, R, C/C++ or equivalent), and evidence of research potential.

Application materials normally include academic transcripts, a curriculum vitae, a personal statement outlining research interests and fit with faculty, and letters of recommendation from academic or research supervisors. Relevant published work or substantive research experience in computational biology, bioinformatics, or related areas strengthens an application. Prospective applicants should consult the programme and Rackham Graduate School for the current list of required materials and any test score policies.

Career prospects

Graduates from this programme move into a broad range of research and leadership roles. Common career destinations include tenure-track and research faculty positions in universities and research institutes, data scientist and research scientist roles in biotechnology and pharmaceutical companies, computational genomics and clinical bioinformatics positions in healthcare organisations, and scientific roles in technology companies and national laboratories. Alumni also enter translational research, regulatory science, consulting, and entrepreneurial ventures where quantitative biology expertise is essential.

Why study at University of Michigan

The University of Michigan offers an especially collaborative environment for computational biology, with faculty and research groups spanning the Medical School, College of Engineering, and the College of Literature, Science, and the Arts. Students benefit from cross-disciplinary centres and institutes that foster interaction between clinicians, experimental biologists and quantitative scientists, access to advanced computing resources and shared experimental facilities, and opportunities to work on clinically relevant problems through partnerships with Michigan Medicine.

Additional advantages include a vibrant research community with regular seminars, symposia and hackathons; structured mentorship and professional development through Rackham and department programmes; and proximity to a thriving biotech and startup ecosystem in Ann Arbor and the broader Michigan region. Prospective students should review faculty research profiles to identify potential mentors whose interests align with their own.