Michigan Technological University

What you'll study

This programme blends core coursework in mathematics, computer science and biology with specialised modules in computational biology and bioinformatics. Early study focuses on foundational topics such as calculus, linear algebra, introductory programming, general chemistry and cell/molecular biology. As you progress you will take intermediate and advanced courses including differential equations, probability and statistics for the life sciences, data structures and algorithms, database design and software development.

Domain-specific and applied modules typically include sequence analysis and comparative genomics, structural bioinformatics, systems biology and network modelling, statistical genomics, machine learning for biological data, computational modelling of physiological systems, and high-throughput data analysis. Laboratory and practicum components teach experimental methods and the handling of biological data, while computing-focused labs emphasise scripting (e.g. Python/R), command-line workflows, and use of high-performance computing resources.

The curriculum commonly culminates in a senior capstone or undergraduate research project, where students design and implement an original computational study or collaborate with faculty on ongoing research. Electives allow students to tailor the degree toward interests such as imaging analysis, synthetic biology, biostatistics, or software engineering for life sciences.

Entry requirements

Applicants should hold a high-school diploma or equivalent with strong preparation in mathematics (calculus recommended) and science (biology and chemistry recommended). Solid experience with algebra and pre-calculus and familiarity with computer programming or willingness to undertake introductory programming are important for success.

Typical admission is through Michigan Technological University's undergraduate admissions process; candidates are considered on the basis of academic record, coursework rigor and supporting materials. Transfer applicants with college-level coursework in calculus, programming and introductory biology are also considered. International applicants must demonstrate equivalent academic preparation and English language proficiency as required by the university.

Career prospects

Graduates from this programme are prepared for quantitative roles in academia, industry and healthcare. Common career paths include bioinformatician, computational biologist, data scientist or analyst in biotechnology and pharmaceutical companies, genomic and clinical research labs, and public health organisations. The combination of programming, statistics and biological knowledge also suits roles in software development for life-science tools, contract research organisations, and companies working on precision medicine or diagnostics.

Many graduates progress to graduate study (master's or PhD) in computational biology, bioinformatics, biostatistics, computer science or related fields. Internship experience, senior projects and undergraduate research at Michigan Tech support transitions into research positions or entry-level industry roles.

Why study at Michigan Technological University

Michigan Technological University is known for strong, hands-on STEM education and an interdisciplinary approach that matches the needs of computational life-science training. Students benefit from access to faculty active in computational biology research, laboratories that combine wet-lab and computational infrastructure, and university high-performance computing resources for handling large biological datasets.

The university emphasises experiential learning: undergraduate research opportunities, capstone design projects and industry-aligned internships help students build practical skills and professional networks. Support services such as career development, internship placement assistance and student organisations focused on computational biology and data science further enhance employability. The programme’s location also provides opportunities to engage with regional research institutions and technology companies.