University of Arizona

What you'll study

The programme provides core training in mathematical modelling, statistical inference and computational methods applied to biological problems. Typical core topics include computational genomics, statistical methods for high‑throughput data, machine learning for biological data, algorithm design, systems and network biology, and data visualisation.

Students normally follow a mix of required core courses and electives drawn from departments across the university, such as Mathematics, Statistics, Computer Science, Molecular & Cellular Biology, and Ecology & Evolutionary Biology. Practical components include programming and software development (Python, R), training in high‑performance computing, and hands‑on analysis of next‑generation sequencing and other omics datasets.

The degree can be completed via a research thesis under the supervision of a faculty advisor or through a coursework/professional option focused on advanced electives and a capstone project. Research areas represented by faculty supervisors at the University of Arizona include comparative genomics, population genomics, phylogenetics, systems biology, computational neurobiology and biomedical data science.

Entry requirements

• Academic background: A relevant bachelor's degree in biology, mathematics, statistics, computer science, engineering or a related quantitative discipline. Strong preparation in calculus and introductory probability/statistics is expected.
• Skills: Practical experience with programming (for example Python, R or MATLAB) and familiarity with basic data analysis are highly desirable.
• Transcripts and references: Official academic transcripts and at least two academic or professional references that can speak to research potential and quantitative ability.
• Statement of purpose: A personal statement describing research interests, relevant experience and reasons for choosing the programme.
• English language: Proof of English proficiency for applicants whose first language is not English, in line with university requirements.
• Additional notes: The programme considers applicants holistically. GRE scores are considered where provided but are not universally required; prospective applicants should consult the department for current test policies. Relevant research experience or coursework in molecular biology, bioinformatics or statistical modelling will strengthen an application.

Career prospects

Graduates enter a range of roles in academia, industry and public sector organisations. Typical career paths include computational biologist, bioinformatics scientist, data scientist for biotechnology or pharmaceutical companies, research analyst in genomics and personalised medicine, and software developer for bioinformatics tools. Many graduates also progress to PhD programmes in computational biology, bioinformatics, biostatistics or related fields.

Employers commonly include university and medical centre research labs, biotech and pharmaceutical companies, clinical genomics services, agricultural and environmental research organisations, and government research agencies. The programme emphasises transferable skills—statistical reasoning, reproducible research practices, and software development—that are valuable across data‑intensive sectors.

Why study at University of Arizona

• Interdisciplinary environment: The university has a strong culture of cross‑department collaboration, allowing students to work with faculty in mathematics, statistics, computer science, molecular biology and ecology.
• Research infrastructure: Access to core facilities and high‑performance computing resources supports large‑scale genomic and computational projects, and there are active research centres focused on bioinformatics and biomedical research.
• Collaborative institutes: Students benefit from connections to campus research institutes and medical research units where translational and applied projects are available for thesis work and capstone projects.
• Hands‑on training: The programme emphasises practical experience with real biological datasets, reproducible workflows and collaborative software development—skills valued by employers and doctoral programmes.
• Location and community: The Tucson campus provides a supportive graduate community with opportunities for seminars, workshops and interdisciplinary projects that enrich training in computational biology.