University of Iowa

What you'll study

The programme blends coursework and research training in mathematical modelling, statistical methods, algorithm design and biological applications. Core topics typically include mathematical methods for biology, statistical inference for high‑dimensional data, computational genomics and sequence analysis, machine learning for biological data, systems biology and dynamical modelling, and practical programming for bioinformatics (commonly using languages such as R and Python).

Students work through a combination of core modules and electives drawn from participating departments, allowing specialisation in areas such as population genetics, structural bioinformatics, transcriptomics, proteomics, network analysis, or high‑performance computing. The degree may be completed with a research thesis based on an original project supervised by faculty across biology, mathematics, statistics, computer science or biomedical engineering, or through a coursework/capstone option depending on the student’s career goals.

• Mathematical Modelling of Biological Systems
• Statistical Methods for Bioinformatics
• Computational Genomics and Sequence Analysis
• Machine Learning and Data Mining in Biology
• Systems Biology and Network Modelling
• Programming for Computational Biology (R, Python, Linux)
• Advanced Topics/Electives: population genetics, structural biology, imaging informatics, HPC

Entry requirements

Applicants are expected to hold a relevant bachelor’s degree in a discipline such as mathematics, statistics, computer science, engineering, or a biological science with substantial quantitative training. Typical preparation includes calculus, linear algebra, probability and statistics, and some programming experience. Background or coursework in molecular biology or genetics is strongly recommended for students without a biology degree.

Application materials usually include academic transcripts, a personal statement outlining research interests and career aims, letters of recommendation, and a CV. Some applicants may be invited for an interview. The programme evaluates candidates on quantitative preparation, research potential and fit with available faculty supervision. International applicants should demonstrate English language proficiency according to the university’s published requirements.

Career prospects

Graduates enter a broad set of careers across academia, industry and the public sector. Common roles include computational biologist, bioinformatics scientist, data scientist for healthcare or biotechnology, genomics analyst, and research scientist in government or academic laboratories. The programme also prepares students for doctoral study in computational biology, biostatistics or related fields.

Alumni work in pharmaceutical and biotech companies, healthcare analytics teams, research institutes and university laboratories. The combination of quantitative training and biological knowledge is well suited to positions that require analysis of large biological datasets, development of algorithms and tools, and modelling of biological systems.

Why study at University of Iowa

The University of Iowa offers an interdisciplinary environment where faculty from biology, computer science, mathematics, statistics and biomedical engineering collaborate on computational biology research. Students benefit from access to research groups focused on genomics, systems biology and translational science, and from connections with the University of Iowa Hospitals & Clinics for medically oriented projects.

The university provides computational resources, core genomics and imaging facilities, and opportunities to work on collaborative projects with centres such as the Iowa Institute of Human Genetics and other campus research units. Small research groups and regular seminar series create mentoring and networking opportunities for students aiming for careers in research, industry or doctoral study.