Eastern Michigan University

What you'll study

The programme combines core coursework in mathematics, statistics and computer science with applied topics in molecular and systems biology. Typical topics include mathematical modelling of biological systems, statistical methods for biological data, bioinformatics algorithms, genomics and transcriptomics data analysis, machine learning for biological applications, and high-performance computing for large-scale datasets.

Programme structure

• Core modules in applied mathematics and statistical methods for the life sciences
• Computational modules covering programming (commonly Python and R), algorithm design and data structures for bioinformatics
• Applied biology modules such as molecular biology for computational scientists, systems biology and genomics
• Electives that can include machine learning, image analysis, population modelling, or specialised bioinformatics topics
• A substantial research component delivered as a thesis or a capstone project/practicum working with faculty or external partners
• Seminars and journal clubs to develop critical reading and communication skills

Students can expect a balance of theoretical instruction, practical programming labs and hands-on research. Projects often use publicly available biological datasets as well as collaborations with faculty on laboratory-generated or institutional datasets.

Entry requirements

Applicants are normally required to hold a bachelor’s degree in a relevant field such as biology, mathematics, statistics, computer science, engineering or a closely related discipline. Applicants without a directly related undergraduate degree may be considered if they can demonstrate sufficient quantitative and biological preparation.

• Academic transcript showing completion of foundational courses (calculus, linear algebra, introductory statistics); courses in molecular or cellular biology are advantageous
• Demonstrable programming experience (for example coursework or project experience in Python, R, MATLAB or similar)
• Statement of purpose outlining research interests and career goals
• Letters of recommendation from academic or professional referees
• Some applicants may be asked to provide a CV and examples of prior coursework or projects; international applicants should also meet English language proficiency requirements

The programme assesses applications holistically; relevant research experience, quantitative coursework and clear motivation for interdisciplinary work strengthen an application.

Career prospects

Graduates from this programme move into a wide range of roles across academia, industry and the public sector. Typical career paths include:

• Bioinformatics analyst or computational biologist in biotech and pharmaceutical companies
• Data scientist or quantitative analyst in health-care analytics, genomics services or digital health startups
• Research scientist or technician in university and government laboratories working on systems biology, population modelling or computational genomics
• Biostatistician supporting clinical research and public-health studies
• Further study at the doctoral level in computational biology, bioinformatics or related fields

Skills emphasised by the programme—programming, statistical modelling, data wrangling and domain knowledge in biology—are transferable across life-science sectors, hospital systems, regulatory agencies and technology companies.

Why study at Eastern Michigan University

Eastern Michigan University offers an interdisciplinary environment with access to faculty from mathematics, computer science and biological sciences, enabling tailored supervisory arrangements and collaborative research. Class sizes tend to be smaller, allowing for close mentoring and hands-on project supervision.

• Opportunities to work on applied research projects and industry-relevant datasets through faculty labs and regional partnerships
• Emphasis on practical, transferable skills—programming in languages commonly used in bioinformatics, statistical analysis and reproducible research practices
• Proximity to a vibrant regional life-science and healthcare ecosystem provides internship and networking opportunities
• Flexible options that accommodate both thesis-based research and professional practicum routes, supporting students aiming for either research or industry careers

Prospective students benefit from a programme designed to bridge quantitative methods and biological insight, preparing them for technical roles or further research in computational biology and related fields.