University of Iowa

What you'll study

This doctoral programme emphasises rigorous training in mathematical modelling, statistical and machine-learning methods, algorithm development and applied computational analysis of biological data. Coursework and research span topics such as:

• Statistical genomics and population genetics: analysis of high-throughput sequencing data, variant calling, association studies and evolutionary models.
• Systems and theoretical biology: dynamical systems, ordinary and partial differential equation models, stochastic processes and multiscale modelling of biological systems.
• Bioinformatics algorithms: sequence analysis, alignment, assembly, network reconstruction and optimisation techniques.
• Machine learning and data science: supervised and unsupervised learning, deep learning for biological data, dimensionality reduction and reproducible pipelines.
• High-performance and scientific computing: parallel computing, workflow management and software engineering for large-scale biological datasets.
• Experimental and translational perspectives: coursework or rotations that link computational work to wet-lab techniques, clinical datasets or public-health surveillance, depending on research focus.

Programme structure typically combines advanced coursework, participation in interdisciplinary seminars, laboratory rotations or collaborative research placements, a qualifying examination or proposal defence, and an original research dissertation. Students work closely with a primary supervisor and frequently co-supervise across departments to reflect the programme's interdisciplinary nature.

Entry requirements

Applicants are expected to hold a bachelor's or master's degree in a relevant discipline such as mathematics, statistics, computer science, engineering, physics, or biology with substantial quantitative content. Competitive applicants usually demonstrate:

• Strong academic performance in quantitative coursework (calculus, linear algebra, probability/statistics) and programming experience (for example R, Python, C/C++).
• Research experience—this may include undergraduate honours projects, master's theses, publications, or relevant industry research roles.
• A clear statement of research interests that aligns with faculty expertise, and the names of potential supervisors if known.
• Letters of recommendation from academic or professional referees who can speak to research potential.

Standardised tests are considered according to departmental guidance; prospective students should consult the programme for current testing policies and any additional materials such as a CV, coding samples or a portfolio of research. International applicants should meet the university's English language requirements.

Career prospects

Graduates of this PhD enter a range of research-focused and applied careers. Typical career paths include:

• Academic research and teaching in computational biology, bioinformatics or applied mathematics departments.
• Biotechnology and pharmaceutical industry roles in genomics, computational drug discovery, biomarker development and clinical bioinformatics.
• Data science and machine-learning positions that apply quantitative methods to biomedical or health-data problems.
• Government and public-health agencies working on epidemiological modelling, pathogen genomics or population-health analytics.
• Research scientist or technical leadership roles in contract research organisations, core genomics facilities or startups developing computational biology tools.

The programme's combination of computational skills and domain knowledge prepares graduates for both independent research careers and translational roles that bridge computation and experiment.

Why study at University of Iowa

The University of Iowa offers an interdisciplinary environment that brings together mathematicians, statisticians, computer scientists, biologists and clinical researchers. The programme benefits from collaborative centres and resources across the campus, including biomedical research groups, genomic and imaging core facilities, and access to clinical data through the university medical centre.

Students gain mentorship from faculty whose research spans systems biology, statistical genomics, infectious-disease modelling and biomedical data science, and have opportunities to collaborate with neighbouring departments and institutes. The university also provides access to high-performance computing infrastructure and training in research communication and grant writing, supporting development into independent investigators and applied data scientists.