University of San Francisco

What you'll study

The programme combines rigorous coursework in mathematics, statistics and computer science with advanced training in molecular biology, genomics and systems biology, preparing students to tackle complex biological questions with quantitative approaches. Early years focus on core methods—probability and stochastic processes, statistical inference for high‑dimensional data, machine learning for biological data, numerical methods and algorithms—alongside foundational molecular and cellular biology.

• Computational genomics and sequence analysis
• Statistical methods for omics data and experimental design
• Systems biology and dynamical modelling of cellular processes
• Machine learning and deep learning for biological applications
• High‑performance computing and algorithm engineering
• Advanced topics seminars (e.g. single‑cell analysis, structural bioinformatics, population genetics)

Students undertake laboratory rotations or research placements to gain practical exposure to experimental data and biological workflows. After completing required coursework and passing a qualifying or comprehensive exam, students progress to independent research under faculty supervision, culminating in a doctoral dissertation and public defence. Regular seminars, journal clubs and teaching opportunities form part of the training.

Entry requirements

Applicants are expected to hold a relevant master's degree or a strong bachelor's degree with substantial preparation in quantitative disciplines. Typical backgrounds include mathematics, statistics, computer science, physics, engineering, bioinformatics or quantitative biology. Successful candidates normally demonstrate:

• Solid undergraduate preparation in calculus, linear algebra, probability and statistics, and programming experience (e.g. Python, R, C/C++).
• Prior coursework or experience in molecular biology, genetics or related life‑science topics, or evidence of ability to acquire such knowledge rapidly.
• Research experience, for example through undergraduate/graduate projects, publications or industry internships.
• A statement of purpose outlining research interests and fit with faculty; academic transcripts; curriculum vitae; and letters of recommendation.

Applicants whose first language is not English are required to demonstrate proficiency in English. Specific documentation requirements and any optional or required standardised tests are detailed on the programme's admissions page; prospective students are encouraged to contact potential faculty advisors before applying.

Career prospects

Graduates of the PhD programme are prepared for a wide range of research and development careers. Common pathways include:

• Academic research and faculty positions in computational biology, bioinformatics, biostatistics and related fields.
• Research scientist or data scientist roles in pharmaceutical and biotechnology companies, focusing on drug discovery, genomics, biomarker development and personalised medicine.
• Positions in healthcare analytics, clinical informatics and diagnostic companies that apply computational methods to patient and clinical trial data.
• Roles in government research labs, public health agencies, and non‑profit organisations working on population genomics, epidemiology and biosecurity.
• Technical leadership or founding roles in startups that translate computational biology methods into products and services.

The programme's emphasis on quantitative methods, software development and collaborative research also equips graduates for cross‑disciplinary roles in data science and computational engineering outside the life sciences.

Why study at University of San Francisco

The University of San Francisco offers an interdisciplinary environment with small cohorts that encourage close mentorship and collaboration across departments. Located in a global hub for biotechnology, health technology and computational research, students benefit from proximity to industry, research institutes and hospitals in the Bay Area.

• Interdisciplinary training that bridges mathematics, computation and biology, with opportunities to work with faculty across departments.
• Access to modern computational facilities and resources for large‑scale data analysis and high‑performance computing.
• A curriculum that integrates technical training with ethical considerations and social responsibility, reflecting the university’s educational values.
• Opportunities for teaching experience, professional development and engagement with local biotech and healthcare communities to build networks for research translation and employment.