Southern Methodist University

What you'll study

This PhD emphasises quantitative theory and computational practice across molecular, cellular and systems biology. Early-stage coursework typically covers subjects such as advanced applied mathematics (dynamical systems, differential equations), statistical inference and biostatistics, algorithms and data structures, machine learning for biological data, and computational genomics. Core training also includes programming for scientific computing, numerical methods, and high-performance computation.

After coursework, students move into research rotations and select a thesis laboratory. Research topics pursued by students commonly include mathematical modelling of biological networks, population and evolutionary dynamics, systems and synthetic biology, single-cell and population genomics, protein structure prediction and design, bioinformatics algorithm development, and computational neuroscience. Training emphasises rigorous model construction, statistical validation against experimental data, and reproducible software and data practices.

Programme structure typically comprises formal coursework, qualifying and candidacy examinations, original dissertation research supervised by a faculty advisor, and public dissertation defence. Seminar attendance, journal clubs and teaching or mentoring responsibilities are integral parts of the training experience.

Entry requirements

Applicants should normally hold a relevant bachelor’s or master’s degree in mathematics, statistics, computer science, engineering, physics, or the biological sciences. A strong undergraduate record with substantial coursework in calculus, linear algebra, probability and statistics, and programming is expected. Prior research experience—such as a thesis, publications, or laboratory/computational project—is highly desirable.

Typical application materials include a CV, statement of research interests, academic transcripts, and letters of recommendation that address quantitative ability and research potential. International applicants must demonstrate English proficiency according to the university’s language requirements. Where applicable, successful applicants will show the ability to bridge disciplines and to work collaboratively in interdisciplinary teams.

Career prospects

Graduates of the programme are prepared for research-focused careers. Common paths include tenure-track academic positions in departments of computational biology, mathematics, statistics or life sciences; research scientist roles in pharmaceutical, biotechnology and diagnostics companies; data science and machine learning positions in healthcare and technology firms; and roles in government or non-profit research laboratories.

Alumni also move into applied computational roles such as bioinformatics lead, quantitative modeller, computational genomics scientist, and senior data scientist. The combination of rigorous mathematical training and hands-on computational experience makes graduates competitive for careers that require both domain knowledge and advanced quantitative skills.

Why study at Southern Methodist University

Southern Methodist University offers an environment that emphasises interdisciplinary collaboration between biological sciences, applied mathematics and computational groups. Students benefit from faculty whose research spans theoretical and applied computational biology, access to dedicated research centres focused on scientific computation and modelling, and departmental seminar series that connect students with visiting scholars and industry practitioners.

Located in a major metropolitan region, the university provides opportunities to engage with a vibrant biomedical, health-tech and data-science community. Research facilities include high-performance computing resources and laboratory collaborations that allow students to work with experimental data from a range of biological systems. The programme’s focus on rigorous quantitative training combined with applied collaborations prepares students to address contemporary challenges in biology and biomedicine.