Florida State University

What you'll study

This doctoral programme emphasises the integration of mathematical theory, statistical methodology and computational techniques with experimental and observational biology. Early coursework builds a rigorous foundation in advanced probability and statistics, mathematical modelling, numerical analysis and algorithm design, followed by specialised modules in areas such as:

• Genomic and transcriptomic analysis: sequence alignment, variant calling, RNA-seq and single-cell analytics.
• Systems and network biology: dynamical systems, network inference, multi-scale modelling of biochemical and cellular processes.
• Machine learning for biological data: supervised and unsupervised methods, deep learning for imaging and omics, model interpretability.
• Structural and computational biophysics: molecular modelling, docking, simulation and statistical mechanics approaches.
• Ecological and epidemiological modelling: population dynamics, spatial models and stochastic processes.
• High-performance and reproducible computing: parallel algorithms, pipeline development and best practices for reproducible research.

Students typically complete a combination of core and elective coursework, pass qualifying examinations or demonstrate research competency, participate in research rotations or join a research group, and complete an original dissertation under faculty supervision. Training emphasises collaborative projects across departments and use of institutional research infrastructure such as high-performance computing and shared biological datasets.

Entry requirements

Applicants should hold a bachelor's or master's degree in a relevant quantitative or life-science discipline (for example mathematics, statistics, computer science, physics, engineering, bioinformatics, or biology) with strong evidence of quantitative preparation. Typical materials required include:

• Academic transcripts showing strong performance in mathematics, statistics and/or computational courses.
• A curriculum vitae summarising research experience and technical skills (programming languages, software, computing resources).
• Statement of purpose describing research interests, relevant experience and proposed areas of study.
• Letters of recommendation from academic or professional referees familiar with the applicant's research potential.
• Demonstrated ability in programming and data analysis; prior research experience in computational biology or a related area is highly desirable.
• International applicants must meet English language proficiency requirements as set by the university.

Admission is research-competitive. Strong applicants normally show a combination of quantitative coursework, practical computing skills, and prior research or project experience. Funding decisions and assistantships are made during the admissions process or upon entry.

Career prospects

Graduates of the programme are prepared for a wide range of careers that require deep quantitative and computational expertise applied to biology. Common paths include:

• Academic research and teaching at universities and research institutes in fields such as systems biology, bioinformatics, and mathematical biology.
• Industry roles in biotechnology and pharmaceutical companies focusing on drug discovery, genomic analysis, biomarker development and computational modelling.
• Data science and machine learning roles in healthcare, agricultural technology, and computational genomics startups.
• Research scientist positions in government laboratories, public-health agencies and non-profit research organisations working on epidemiology, environmental modelling or bioinformatics infrastructure.
• Technical leadership roles such as bioinformatics pipeline development, scientific computing management and interdisciplinary research coordination.

The doctoral training emphasises transferable skills — statistical reasoning, software development, data stewardship, scientific communication and grant writing — that support diverse career trajectories.

Why study at Florida State University

Florida State University offers this doctoral programme in a broadly interdisciplinary environment where students can draw on expertise across mathematics, statistics, biological sciences, and computer science. The university provides access to institutional resources such as high-performance computing, core sequencing and imaging facilities, and collaborative research centres that support cross-disciplinary projects.

Students benefit from close mentorship with faculty actively publishing in computational biology and related fields, opportunities for collaboration with experimental groups, and a collegiate research community that encourages interdisciplinary training. The programme's structure is designed to produce researchers capable of developing new quantitative methods and applying them to pressing biological problems, preparing graduates for both academic and industry careers.