University of Kansas

What you'll study

This programme combines coursework in quantitative methods, algorithmic approaches and biological sciences. Core topics typically include statistical genomics, machine learning for biological data, sequence analysis and alignment, biological data mining, systems and network biology, mathematical modelling of biological systems, structural bioinformatics, and high-throughput data analysis. Students gain practical skills in programming (commonly R and Python), applied statistics, database management, and use of bioinformatics tools and pipelines.

The degree is structured to include advanced coursework, hands-on laboratory or computing projects, and a substantial research project or thesis supervised by faculty. Elective options allow students to focus on areas such as population genetics, evolutionary bioinformatics, computational systems biology, imaging informatics, or clinical and translational bioinformatics. Many students also complete practicum-style projects that partner with research groups in the life sciences or with industry collaborators.

Entry requirements

Applicants are normally expected to hold a bachelor’s degree in a relevant discipline such as biology, biochemistry, mathematics, statistics, computer science, engineering or a closely related field. A solid foundation in undergraduate-level calculus, linear algebra, programming and introductory statistics is advantageous. Selection is based on academic transcripts, a personal statement outlining research and career interests, and academic or professional references.

Applicants whose first language is not English must demonstrate proficiency through an approved English language test unless exempt. The programme may also require a CV or résumé and, for some applicants, evidence of prior research or project experience in quantitative or computational biology.

Career prospects

Graduates leave prepared for roles that require quantitative analysis of biological data and computational problem-solving. Typical job titles include bioinformatics analyst, computational biologist, genomic data scientist, biostatistician, research scientist in industry or academia, and software engineer for life-science applications. Alumni work in biotechnology and pharmaceutical companies, clinical and public-health laboratories, academic research groups, government agencies, and start-ups developing tools for genomics, precision medicine and systems biology.

The programme also provides a strong foundation for students who wish to pursue doctoral study (PhD) in computational biology, bioinformatics, biostatistics or related disciplines.

Why study at University of Kansas

The University of Kansas offers an interdisciplinary environment where students can work across departments such as biology, mathematics and computer science and collaborate with researchers at the KU Medical Center. Students benefit from access to institutional core facilities, research-active faculty with diverse computational and experimental interests, and campus high-performance computing resources.

KU supports hands-on training through project partnerships and research assistantships, and the smaller cohort sizes in specialised masters programmes provide close mentorship and opportunities to develop publishable research or industry-relevant portfolios. The programme’s location and institutional links also facilitate connections with regional biotech employers and clinical research initiatives.