University of San Diego

What you'll study

This interdisciplinary master's combines coursework in mathematics, computer science and molecular biology with hands-on experience in data analysis and modelling. Core topics typically include statistical methods for biological data, machine learning for life sciences, computational genomics and sequence analysis, mathematical modelling of biological systems, and programming for bioinformatics (Python/R). Elective modules allow specialisation in areas such as systems biology, structural bioinformatics, single-cell analysis, metagenomics, and imaging analysis.

The programme normally offers a mix of lectures, seminars and laboratory/computational practicums. Students complete advanced coursework and either a research thesis or a project-based capstone that integrates computational analysis with a biological question. Practical training emphasises reproducible workflows, high-performance computing, data visualisation and familiarity with common bioinformatics tools and databases.

• Core modules: Biostatistics and Experimental Design; Algorithms in Bioinformatics; Mathematical Models in Biology; Computational Genomics; Programming for Computational Biology.
• Typical electives: Systems Biology and Network Analysis; Machine Learning for Biomedical Data; Structural Bioinformatics; Single-Cell and Spatial Transcriptomics; Microbiome Bioinformatics.
• Research component: thesis or industry/research practicum with a substantive computational project and presentation of results.

Entry requirements

Applicants should normally hold a bachelor's degree in a relevant discipline such as biology, mathematics, statistics, computer science, engineering or a closely related field. Successful applicants typically demonstrate competence in quantitative coursework (calculus and linear algebra), basic statistics, and some programming experience (for example Python, R or MATLAB). Background in molecular biology or genetics is beneficial for candidates coming from a quantitative background.

Required application materials generally include a transcript, a personal statement describing research interests and goals, a current CV, and academic or professional references. The programme may consider applicants with non-traditional backgrounds if they can show evidence of quantitative and biological preparation or are willing to complete specified prerequisites. Some applicants pursue conditional admission and complete bridging courses before full entry.

Career prospects

Graduates are prepared for a wide range of roles in academia, industry and the public sector. Typical career paths include bioinformatics analyst, computational biologist, data scientist in biotech or pharmaceuticals, genomic data analyst, biostatistician, and research associate in interdisciplinary laboratories. The degree also provides a strong foundation for admission to PhD programmes in computational biology, systems biology or related fields.

Because the curriculum emphasises practical data skills and collaborations with experimental groups, graduates are well suited to positions that require analysing next-generation sequencing data, developing analytical pipelines, building predictive models for biological systems, or working on translational projects in precision medicine and public health.

Why study at University of San Diego

The University of San Diego offers this programme within a compact, research-active environment that fosters interdisciplinary collaboration across biology, mathematics and computer science. Its location in the San Diego region places students close to a major biotechnology and life-sciences cluster with opportunities for internships and partnerships with research institutes and companies.

Students benefit from small class sizes and direct access to faculty who are active in computational and experimental research, as well as core computational resources and laboratory facilities. The programme emphasises experiential learning through research projects and practicum opportunities, helping students build a professional portfolio of computational analyses and reproducible code that employers and doctoral programmes value.

In addition, the university's collaborative culture and support services — such as career advising and research mentorship — help students translate technical training into real-world experience and career advancement in both industry and academia.