University of Mississippi

What you'll study

This programme combines coursework and research to build competency in mathematical modelling, statistical inference, algorithm development and the analysis of high-throughput biological data. Core topics typically include mathematical biology and dynamical systems, statistical methods for bioinformatics, machine learning for biological data, computational genomics, and programming for scientific computing. Students also study supporting subjects such as probability, linear algebra, numerical methods, and database and software engineering for handling large datasets.

Teaching is delivered through a mix of lectures, hands-on computational laboratories and research seminars. Most students undertake a substantial research project or thesis supervised by faculty from mathematics, biology or computer science, allowing direct experience in applying quantitative tools to problems such as gene expression analysis, population genetics, epidemiological modelling or systems biology. There is usually the option of a non-thesis route with additional coursework and a capstone project for those prioritising professional practice.

Entry requirements

• Academic background: A bachelor’s degree in mathematics, statistics, computer science, biology, engineering or a closely related field. Candidates without a directly related degree may be admitted conditionally if they demonstrate sufficient quantitative and biological preparation.
• Prerequisites: Undergraduate coursework in calculus, linear algebra, introductory probability and statistics, and some programming experience (for example Python, R or MATLAB). Introductory molecular biology or genetics is strongly recommended for students without a life-sciences background.
• Application materials: Official transcripts, a personal statement outlining research interests and goals, and letters of recommendation. Applicants should emphasise quantitative skills and any relevant research or programming experience.
• English language proficiency: International applicants are required to demonstrate English proficiency through approved tests or institutional waivers where applicable.
• Other considerations: Research experience, demonstrated coding ability, and a clear alignment with potential faculty supervisors strengthen an application. The programme may admit students to either thesis or non-thesis tracks depending on their career goals.

Career prospects

Graduates are prepared for roles that require quantitative analysis of biological data and development of computational tools. Common job titles include bioinformatician, computational biologist, data scientist in life sciences, biostatistical analyst, and software engineer for scientific applications. Employers span academic research groups, biotechnology and pharmaceutical companies, healthcare organisations, public health agencies, and contract research organisations.

Many students use the master’s as preparation for doctoral study in computational biology, biostatistics or related disciplines, while others move directly into industry positions where skills in programming, data analysis, machine learning and domain knowledge of genomics or systems biology are in demand.

Why study at University of Mississippi

• Interdisciplinary mentorship: The University of Mississippi offers cross-departmental supervision, enabling students to work with faculty in mathematics, statistics, biology and computer science on real-world biological problems.
• Research-led training: Students benefit from active research programmes and opportunities to contribute to faculty-led projects in genomics, ecological modelling, epidemiology and systems biology.
• Computational resources and facilities: The university provides access to modern computational infrastructure and software tools needed for large-scale data analysis and modelling, as well as core laboratory facilities for collaborators in the life sciences.
• Career support and industry links: Graduate students can access career services, internship opportunities and regional industry partnerships that help transition into roles in biotech, healthcare and government research.
• Personalised cohort experience: The programme’s size encourages close faculty interaction and tailored training paths, preparing students for either research-intensive or professionally oriented careers.