University of Mississippi

What you'll study

The programme combines advanced coursework, hands-on computational training and an extended independent research dissertation. Core themes include mathematical modelling of biological systems, statistical and machine-learning approaches for high-dimensional biological data, algorithm and software development for sequence and structural bioinformatics, and systems and population-level modelling.

• Typical coursework: advanced mathematical methods for biology, stochastic processes and dynamical systems, statistical inference and Bayesian methods, machine learning for biological data, algorithms for bioinformatics, and numerical methods.
• Computational skills: programming in languages commonly used in the field (for example Python, R and C/C++), high-performance and parallel computing, database management, and reproducible research workflows.
• Domain-focused modules: genomics and transcriptomics analysis, proteomics and structural bioinformatics, population genetics and evolutionary modelling, systems biology and network analysis.
• Research training: laboratory or computational rotations (where available), participation in research seminars and journal clubs, qualifying examinations, and defence of an original dissertation based on independent research under a faculty advisor.

Entry requirements

Applicants are normally expected to hold a strong bachelor’s degree in mathematics, statistics, computer science, engineering, or a life science; many applicants also have a relevant master's degree. Admissions favour candidates with demonstrated quantitative and programming skills, evidence of research potential, and a clear statement of research interests that aligns with faculty expertise.

• Academic background: undergraduate or postgraduate training that includes calculus, linear algebra, probability/statistics, and some programming experience.
• Research experience: prior research, publications, or independent project work is highly desirable and strengthens an application.
• Supporting materials: academic transcripts, letters of recommendation, a CV, and a research statement. International applicants must demonstrate English language proficiency according to university regulations.
• Funding and assistantships: the department typically offers graduate teaching or research assistantships to qualified PhD students; applicants should consult the department for details on funding processes and expectations.

Career prospects

Graduates are prepared for a broad range of careers that require interdisciplinary quantitative and biological expertise. Common pathways include academic research and teaching, postdoctoral positions, and roles in industry and government.

• Academia and research: faculty positions, postdoctoral appointments in computational biology, biostatistics or related fields.
• Industry: data scientist, bioinformatics scientist, computational biologist, and roles in biotechnology, pharmaceuticals, and diagnostic companies working on genomics, drug discovery, and translational research.
• Public sector and healthcare: positions in public health agencies, research institutes, and national laboratories applying modelling and data analysis to epidemiology, population health and environmental biology.
• Tech and startups: roles developing software and algorithms for biological data, or founding and joining startups that commercialise computational biology tools and services.

Why study at University of Mississippi

The University of Mississippi offers an interdisciplinary environment that brings together faculty from mathematics, computer science, biology and related departments, enabling collaborative projects and cross-disciplinary supervision. The campus provides access to institutional computational resources and core research facilities that support genomics and high-throughput data analysis, as well as opportunities to work on regionally relevant biological problems.

• Interdisciplinary faculty: supervision from researchers with expertise spanning theoretical modelling, statistical genomics and applied bioinformatics.
• Research infrastructure: access to university computing resources, shared lab and sequencing facilities, and training in reproducible computational workflows.
• Collaborative culture: seminars, workshops and collaborations across departments foster networking and professional development in both academic and applied settings.
• Career support: the department and university career services assist students in preparing for academic, industry and government careers through mentoring, placement support and professional development activities.