Rochester Institute of Technology

What you'll study

This programme blends foundational courses in molecular and cellular biology with rigorous training in calculus, linear algebra, probability and statistics, and computer science. Typical topics and modules include:

• Core biology: cell and molecular biology, genetics, biochemistry and laboratory techniques to provide biological context for computational work.
• Mathematics and statistics: calculus sequence, linear algebra, differential equations, mathematical modelling, and statistical inference for analysing biological data.
• Computer science and programming: data structures and algorithms, scientific programming (Python, R), database systems, and software engineering practices relevant to bioinformatics.
• Bioinformatics and computational biology: sequence analysis, genomics and transcriptomics, structural bioinformatics, network and systems biology, machine learning applications to biological data, and algorithmic approaches for biological problems.
• Laboratory and practicum experiences: wet-lab courses that teach experimental methods; computational lab courses that emphasise reproducible analysis, pipeline development and high-performance computing.
• Capstone and research: a senior capstone or research project integrating computation and experiment—students model biological systems, analyse large omics datasets, or develop bioinformatics tools, often in collaboration with faculty or industry partners.

The curriculum is designed to be interdisciplinary and hands-on; students typically take elective tracks or minors in related fields such as computer science, data science, or molecular biology to tailor their skills.

Entry requirements

Applicants should hold a completed secondary school diploma with a strong background in mathematics and the sciences. Typical preparation includes:

• High-level mathematics courses including precalculus and preferably calculus.
• Laboratory science courses such as biology and chemistry.
• Some experience with programming or computer science is advantageous but not always required; willingness to learn programming in the first year is expected.
• A competitive academic record in STEM subjects. Admissions will also consider statements of purpose, recommendations and any relevant extracurricular experience such as research, coding projects, or internships.
• For applicants whose first language is not English, demonstrated English proficiency through an accepted test or other institutional pathways is required.

RIT places emphasis on experiential learning, so demonstrated interest in hands-on projects or co‑op/internship readiness strengthens an application.

Career prospects

Graduates from this programme are prepared for a range of roles at the interface of biology and computation. Common career paths include:

• Bioinformatics analyst or scientist in academic, clinical or industrial laboratories analysing genomics, proteomics or other omics datasets.
• Computational biologist developing and applying models of biological systems, population dynamics, or structural biology problems.
• Data scientist or machine learning engineer in biotech, pharmaceutical companies, healthcare analytics or startups working on biomedical data.
• Software engineer or developer specialising in scientific software, databases for biological data, or cloud-based analysis platforms.
• Research assistant or technician roles leading to graduate study (MS/PhD) in bioinformatics, computational biology, biostatistics or related fields.

The programme’s emphasis on practical projects, computing skills and cooperative education opportunities supports transitions into both industry and advanced academic training.

Why study at Rochester Institute of Technology

RIT is known for its strong emphasis on experiential and career-focused education. Students in this major benefit from:

• Cooperative education and internships: structured co‑op programmes and partnerships with biotech companies, healthcare institutions and research labs provide real-world experience and industry connections.
• Interdisciplinary environment: close collaboration between computing, life sciences and engineering faculties enables projects that span wet lab and computational work.
• Computing and lab resources: access to modern computing facilities, high-performance computing resources and institutional core labs for molecular and sequencing work.
• Applied research opportunities: chances to work alongside faculty on funded research or start-up focused projects that produce tangible outputs for portfolios or publications.
• Career support: career services, industry workshops and alumni networks that help students secure internships and full‑time positions in biotechnology, healthcare and data science sectors.

Together these features make RIT a practical choice for students seeking an education that blends rigorous quantitative training with applied biological problems and strong pathways into employment or graduate study.