University of Oregon

What you'll study

This master's programme blends mathematical modelling, statistical inference and computational methods with core molecular and cellular biology. You will learn to analyse large biological data sets, develop algorithms and build dynamic models that explain biological systems.

Typical modules and topics

• Mathematical Biology: deterministic and stochastic models, population and epidemiological models, dynamical systems applied to biological questions.
• Computational Genomics and Bioinformatics: sequence analysis, genome assembly, variant calling, transcriptomics and functional annotation workflows.
• Statistical Methods for Biological Data: statistical inference, experimental design, regression, mixed models and Bayesian approaches tailored for biological data.
• Algorithms and Machine Learning for Biology: algorithm design, supervised and unsupervised learning, dimensionality reduction and practical applications in omics and imaging data.
• Systems and Synthetic Biology: network inference, pathway modelling and integration of multi-omic data to study regulatory systems.
• Computational Tools and High-Performance Computing: practical training in Python/R, workflow management, parallel computing and reproducible research practices.
• Laboratory and Experimental Design (optional/paired courses): introductory molecular biology techniques and experimental considerations for computational analysis.

Programme structure typically includes core coursework, electives that allow specialisation (for example genomics, ecological modelling or machine learning), and a substantial culminating component such as a research thesis or a project-based capstone conducted with faculty supervision.

Entry requirements

Applicants are usually expected to hold a bachelor's degree in a relevant field such as biology, mathematics, statistics, computer science, engineering or a closely related discipline. Successful applicants typically demonstrate the following:

• A solid foundation in calculus, linear algebra, probability and statistics.
• Programming experience in at least one language commonly used in computational biology (for example Python, R, or MATLAB).
• Undergraduate coursework in molecular or cellular biology is highly desirable for students focusing on bioinformatics applications to genomics or wet-lab collaborations.
• Academic transcripts showing good standing; competitive programmes expect strong grades in quantitative and relevant science courses.
• Letters of recommendation, a statement of purpose outlining research or career goals, and a CV. Some applicants may be asked for examples of past coding or data-analysis work.

For international applicants, proof of English language proficiency is required according to university policy. The programme may consider applicants from diverse academic backgrounds through remedial or bridging coursework where necessary.

Career prospects

Graduates of this programme are equipped for a variety of roles in academia, industry and government. Typical career paths include:

• Computational biologist or bioinformatician in biotech, pharmaceutical companies or clinical research labs.
• Data scientist or machine learning engineer focusing on life-science applications, biomedical imaging or health data analytics.
• Biostatistician or quantitative analyst working with clinical trials, public health agencies or research institutions.
• Research technician or research scientist in university labs, national laboratories and independent research institutes.
• Further research training through PhD programmes in computational biology, bioinformatics, systems biology, statistics or related fields.

Hands-on project work and thesis research often lead directly to research collaborations, publications and opportunities with regional biotech firms or healthcare partners.

Why study at University of Oregon

The University of Oregon offers a genuinely interdisciplinary environment where faculty from biology, mathematics, statistics and computer science collaborate on computational life-science problems. Students benefit from access to shared research facilities, high-performance computing resources and supervised research projects that span wet-lab and dry-lab approaches.

The campus culture emphasises collaborative training, reproducible research practices and professional development. Small cohort sizes and active faculty mentoring help students tailor the curriculum to their interests, whether that is genomics, ecological modelling, translational research or advancing to doctoral study. Proximity to a growing regional life-science ecosystem also creates opportunities for internships and industry partnerships.

Overall, the programme is well-suited to students seeking rigorous quantitative training applied directly to contemporary biological challenges, with pathways into research, industry and continued academic study.