University of San Diego

What you'll study

The programme combines rigorous coursework with sustained original research. Core topics typically include advanced mathematical methods for biology (differential equations, stochastic processes), statistical inference for high-dimensional data, machine learning and data mining, algorithms for sequence and structure analysis, and computational systems biology. Students also cover molecular and cellular biology fundamentals so that quantitative models are grounded in experimental biology.

• Core modules: Mathematical modelling of biological systems, statistical methods for bioinformatics, algorithms and data structures for biological data, and computational genomics.
• Advanced electives: Structural bioinformatics, single-cell and spatial transcriptomics, network biology, evolutionary models, and Bayesian methods for complex data.
• Practical training: Programming for scientific computing (Python, R), high-performance computing, reproducible research, and pipeline development for next-generation sequencing data.
• Research components: Rotations or practicum projects in faculty laboratories, preparation for and completion of a qualifying/comprehensive exam, and dissertation research leading to original contributions in theory, methods, or application.

Entry requirements

Applicants should normally hold a relevant master's degree or a strong bachelor’s degree with considerable quantitative and laboratory experience. Typical backgrounds include mathematics, statistics, computer science, bioinformatics, molecular biology, or related disciplines.

• Demonstrated competence in mathematics (calculus, linear algebra, probability) and programming (Python, R, or equivalent).
• Prior coursework or experience in statistics, algorithms, or introductory computational biology is highly desirable.
• A statement of purpose outlining research interests, academic transcripts, and at least two academic references are required. Evidence of research experience (publications, project reports, or supervised research) strengthens an application.
• International applicants must meet English language proficiency standards. Specific standardised test requirements (if any) and minimum scores should be checked with the department.

Career prospects

Graduates are prepared for research and leadership roles across academia, industry and the public sector. Career paths commonly followed include university faculty and postdoctoral positions, computational biologist or bioinformatician roles in biotechnology and pharmaceutical companies, data scientist positions in healthcare and diagnostics, and research scientist roles in government or non-profit research institutes.

• Industry roles: bioinformatics scientist, computational genomics analyst, machine learning engineer focused on biomedical data, and bioinformatics pipeline developer.
• Academic and research careers: postdoctoral researcher, principal investigator, and interdisciplinary collaborator in systems biology or theoretical biology groups.
• Other pathways: clinical and translational data scientist, regulatory science analyst, and technical lead in startups leveraging omics or health data.

Why study at University of San Diego

The University of San Diego offers a closely mentored, interdisciplinary environment that bridges mathematics, computer science and the biological sciences. Students benefit from small cohort sizes and direct access to faculty whose research spans computational modelling, genomics and systems biology. The university’s location in the San Diego region provides proximity to a large and active biotechnology and life sciences community, opening opportunities for collaborations, internships and engagement with research institutes in the area.

Facilities supporting this programme typically include high-performance computing resources, modern molecular biology laboratories for collaborative projects, and opportunities to participate in seminar series and cross-departmental research groups. The programme emphasises reproducible, open-science practices and prepares students to communicate results to both technical and applied audiences.