University of Oregon

What you'll study

The programme combines advanced coursework in mathematics, statistics, computer science and molecular/organismal biology with original dissertation research. Core topics commonly studied include mathematical modelling of biological systems (deterministic and stochastic dynamics), statistical inference for high-dimensional biological data, computational genomics and transcriptomics, machine learning for biological data, algorithm design and analysis for bioinformatics, systems and network biology, and methods for single-cell and population-scale data.

Students typically take a mix of required and elective courses drawn from multiple departments to build depth in both quantitative methods and biological application areas. Training emphasises practical skills in data analysis, software development and high-performance computing, including reproducible workflows, experimental design and statistical validation. Seminar series and journal clubs expose students to current literature across fields.

The research component begins early and culminates in a doctoral dissertation. Students work with faculty mentors from mathematics, statistics, computer science, biology, human physiology or related units, often forming interdisciplinary committees. Progress milestones usually include qualifying examinations, proposal/qualifying papers or thesis proposals, and annual progress reviews. Teaching or mentoring experience is typically part of graduate training.

Entry requirements

Applicants are expected to hold a bachelor’s or master’s degree with substantial quantitative training. Typical preparation includes undergraduate coursework in calculus, linear algebra, differential equations, probability and statistics, and programming experience; background in molecular or cellular biology is advantageous but not always required if the candidate demonstrates clear interest and aptitude for biological problems.

• Academic transcripts showing strong performance in relevant quantitative and/or biological subjects.
• Letters of recommendation that address research potential and preparation for a demanding interdisciplinary PhD programme.
• A statement of purpose describing research interests, relevant experience (courses, projects, coding, publications) and preferred faculty or research groups at the university.
• A CV outlining research, internships, publications, software contributions or relevant work experience.
• Research experience is strongly preferred; candidates with independent projects, publications or substantial lab/computational work are competitive.

The programme evaluates applicants holistically; standardised tests may be optional or considered in context of the application. Prospective students are encouraged to review faculty research interests and contact potential advisers where appropriate.

Career prospects

Graduates of the programme pursue a broad range of careers. Many continue in academic research as postdoctoral scholars and faculty in departments of biology, mathematics, statistics, computer science or interdisciplinary centres. Others move to industry roles in biotechnology, pharmaceutical companies, health informatics firms and start-ups, applying skills in computational genomics, drug discovery, clinical data science and bioinformatics software development.

Additional career paths include positions in government and national laboratories, public health agencies, scientific consulting, and data science roles in non-life-science sectors where quantitative biological expertise is valued. The combination of computational, statistical and biological training also prepares graduates for leadership roles in R&D and for bridging collaborations between experimental and computational teams.

Why study at University of Oregon

The University of Oregon offers an interdisciplinary environment that brings together faculty from mathematics, statistics, computer science and the biological sciences. Students benefit from collaborative research groups, access to institutional core facilities for genomics and imaging, and university-supported research computing resources for large-scale data analysis.

The campus fosters close faculty-student interactions typical of research-intensive but accessible graduate programmes; students can tailor training by selecting coursework and mentors across departments. Regional collaborations with nearby medical and research institutions broaden opportunities for applied projects and internships. Additionally, the university’s emphasis on reproducible research and open-source software development equips graduates with practical skills valued by both academic and industry employers.

Support structures such as seminars, workshops on computational methods, and professional development resources help students build teaching experience, grant-writing skills and career readiness while pursuing their doctoral research.