University of Cincinnati

What you'll study

The programme combines rigorous coursework in mathematics, statistics and computer science with advanced training in molecular and systems biology. Core topics typically include mathematical modelling of biological systems, stochastic processes, dynamical systems, computational genomics, statistical learning, algorithm design for biological data, and high-performance computing for large datasets. Students take seminars in current computational biology research, participate in journal clubs and attend interdisciplinary colloquia across departments.

Beyond coursework, the PhD emphasises independent research. Students undertake research rotations or laboratory placements to identify a dissertation group, pass qualifying or candidacy examinations, and then complete an original research thesis under the supervision of a faculty advisor. Training in reproducible research practices, scientific communication, grant writing and ethics in computational biology is integrated throughout the programme.

• Mathematical Biology and Dynamical Systems
• Stochastic Processes in Biology
• Statistical Methods for High-dimensional Data
• Computational Genomics and Transcriptomics
• Machine Learning and Data Mining for Biological Data
• Algorithms for Bioinformatics and Sequence Analysis
• High-performance and Cloud Computing for Science
• Systems Biology and Network Modelling

Entry requirements

Applicants should hold a bachelor’s or master’s degree in mathematics, statistics, computer science, engineering, physics, biology with substantial quantitative coursework, or a closely related discipline. Strong preparation in multivariable calculus, linear algebra, differential equations, probability and statistics is expected, together with programming experience (for example Python, R, C/C++ or MATLAB).

Typical application materials include official academic transcripts, a statement of purpose outlining research interests, a curriculum vitae, and at least two academic references. Research experience in quantitative biology, bioinformatics or computational modelling is highly desirable. Proof of English-language proficiency is required for applicants whose first language is not English, in line with university regulations.

Admission is competitive and candidates are evaluated on academic preparation, fit with faculty research areas, and the potential to carry out independent interdisciplinary research. Funding is commonly provided through graduate assistantships, fellowships or research grants; applicants are encouraged to contact potential supervisors about research openings before applying.

Career prospects

Graduates from this PhD programme pursue careers across academia, industry and government. Common roles include tenure-track faculty positions, postdoctoral researchers, computational biologists and bioinformaticians in biotechnology and pharmaceutical companies, data scientists in health‑care analytics, and research scientists in national laboratories and research hospitals. Graduates also work in clinical and translational research units, bioinformatics core facilities, and startups developing computational tools for genomics and personalised medicine.

The programme’s emphasis on transferable skills—advanced quantitative modelling, statistical inference, software development, and scientific communication—prepares alumni for leadership roles in multidisciplinary teams and for careers that require the integration of complex biological data with computational methods.

Why study at University of Cincinnati

The University of Cincinnati offers a strongly interdisciplinary environment for computational biology, with collaborations across the Department of Mathematical Sciences, Computer Science, Biological Sciences, and the College of Medicine. Proximity and formal research links with major regional clinical and research institutions, including Cincinnati Children’s Hospital Medical Center, provide access to clinical datasets, experimental collaborators and translational research projects.

Students benefit from access to university computational resources, core genomics and imaging facilities, and regular interdisciplinary seminars and workshops. The graduate community emphasises mentorship, collaborative research teams and professional development opportunities such as teaching experience, grant writing workshops and industry networking events. Funding support through research and teaching assistantships helps enable full-time research training.

Overall, the University of Cincinnati’s PhD in Biomathematics, Bioinformatics, and Computational Biology is suited to students seeking a research-intensive, interdisciplinary training pathway that bridges quantitative methods and modern biological problems.