B.S. in Computational Biology

4 Years On Campus Bachelors Program

Rensselaer Polytechnic Institute

Program Overview

B.S. in Computational Biology at Rensselaer Polytechnic Institute combines biology, computer science, information technology, and statistical modeling to prepare students to analyze large biological datasets and solve complex problems in the life sciences. The program suits students interested in the intersection of biology and computation, with two concentration options—Biomolecular Systems for areas such as protein structure, drug discovery, and molecular interactions, and Ecological Systems for large-scale environmental and ecological challenges.

Curriculum Structure

Year 1: Students establish the mathematical, biological, chemical, and computational foundations needed for the major, with subjects such as BIOL 1010 Introduction to Biology, BIOL 1016 Introduction to Biology Computational Laboratory, and MATH 1010 Calculus I. The computational laboratory introduces real-world biological datasets and develops skills in managing, visualizing, analyzing, and interpreting biological data, culminating in a collaborative research project.

Year 2: Students progress into more specialized biological and computational concepts, including BIOL 2120 Introduction to Cell & Molecular Biology, which examines cellular structure and function, metabolism, genetics, and the chemical basis of heredity. Coursework continues to strengthen quantitative and computing foundations while connecting biological concepts with data-driven approaches.

Year 3: Students move toward advanced computational biology and begin tailoring their studies through the Biomolecular Systems or Ecological Systems concentration. Depending on their selected pathway, students can develop computational approaches for molecular structures, biological interactions and drug discovery, or for large-scale ecological systems and environmental change.

Year 4: The final year emphasizes advanced application, research preparation, and the program's culminating laboratory experience. Students apply scientific models, simulations, quantitative methods, and data analysis to biological problems, while selected students may undertake independent faculty research, an internship, or co-op.

Focus areas: Biomolecular systems, ecological systems, computational biology, biological data analysis, statistical modeling, scientific modeling and simulation, bioinformatics, drug discovery, environmental change

Learning outcomes: Scientific process, quantitative reasoning, biological data analysis and visualization, statistical models and simulations, interdisciplinary problem-solving, scientific communication, collaboration, ethical understanding of science and society

Professional alignment (accreditation): The official Rensselaer program page identifies the B.S. in Computational Biology as a School of Science degree, but does not state a separate professional or program-specific accreditation for this bachelor's program.

Reputation (employability rankings): Rensselaer's official rankings page reports a #15 Best Career Placement ranking from Princeton Review's 2021 ranking and a #6 Most Hired From by Firms ranking from DesignIntelligence's 2020 ranking; these are university-level rankings rather than rankings specific to the Computational Biology program.

Experiential Learning (Research, Projects, Internships etc.)

The B.S. in Computational Biology at Rensselaer Polytechnic Institute combines biology, computer science, statistics, and information technology, allowing students to develop practical skills for analyzing biological systems and large datasets. Students learn to use computational and statistical models, work with real-world biological data, and apply interdisciplinary methods to biological problems. The program includes an advanced laboratory culminating experience, while eligible students can also gain deeper experience through faculty research, internships, or co-ops.

These program-specific opportunities give students multiple ways to develop computational, research, and collaborative skills:

Computational data analysis: BIOL 1016 – Introduction to Biological Computational Laboratory introduces students to real-world biological datasets and develops skills in managing, visualizing, analyzing, and interpreting biological data; the course culminates in a collaborative research project.
Models and simulations: The program specifically develops the ability to understand and apply statistical models and system-level models or simulations to biological problems.
Advanced laboratory experience: Culminating advanced laboratory course is required for all Computational Biology students and provides preparation for research-oriented careers.
Research opportunities: Students may undertake independent research in faculty laboratories, giving them opportunities to work on active computational biology research rather than only classroom exercises.
Internships and co-ops: Selected students can complete their practical research experience through an internship or co-op, in addition to faculty laboratory research.
Center for Computational Innovations: The Center for Computational Innovations (CCI) provides high-performance cluster computing and data-analytics capabilities. Its AiMOS supercomputer is an IBM POWER9-based system designed for large-scale computational research, and CCI supports research involving students and multiple disciplines.
Biotechnology research facilities: The Center for Biotechnology and Interdisciplinary Studies (CBIS) provides an interdisciplinary environment connecting life sciences, computation, engineering, and other fields. Its undergraduate research program offers independent-study fellowships, including paid research opportunities in CBIS laboratories.
Research centers: Computational Biology students can benefit from RPI research environments including CBIS, CCI, the Rensselaer Exploratory Center for Cheminformatics Research (RECCR), the Center for Modeling, Optimization, and Computational Analysis (MOCA), and the Scientific Computation Research Center (SCOREC), which support computational, data-driven, biotechnology, and biological-systems research.
Collaborative research: RPI's Biological Sciences department emphasizes data-driven biological sciences, broad collaboration, and quantitative biological research, giving students an environment closely aligned with the interdisciplinary nature of computational biology.
Concentrations: Students can specialize through Biomolecular Systems, applying computational methods to proteins, nucleic acids, glycans, biomembranes, and drug discovery, or Ecological Systems, applying computational methods to large-scale ecological and environmental-change problems.

Progression & Future Opportunities

The B.S. in Computational Biology combines biology, computer science, and information technology, preparing graduates to analyze biological systems using computational and statistical methods. Rensselaer states that graduates entering the workforce are hired across pharmaceutical, medical informatics, agricultural bioinformatics, government and national laboratories, database management, and biotechnology sectors.

Career and Employment Opportunities: Graduates can pursue roles such as computational biologist, bioinformatics analyst, biological data analyst, research scientist, or related positions in biotechnology and pharmaceutical organizations. Rensselaer also encourages graduates to continue into the growing number of computational biology graduate programs worldwide.

Career services: Rensselaer’s Center for Career and Professional Development provides employment resources and maintains Handshake, the university’s official job board, where students can access positions from on-campus and external employers. The center also publishes graduate hiring and starting-salary reports.
Employment statistics and salaries: Rensselaer publishes annual post-graduation outcomes and starting-salary reports, with data collected up to six months after graduation. The university does not publish a separate employment rate or starting-salary figure specifically for the B.S. in Computational Biology on the program page.
Industry partnerships: Rensselaer’s Center for Biotechnology and Interdisciplinary Studies (CBIS) works with industry partners on biotechnology research, including pharmaceuticals, therapeutics, diagnostics, medical devices, and tissue regeneration.
Specific research partnership: Since 2014, RPI’s CBIS has maintained an institute-wide partnership with Icahn School of Medicine at Mount Sinai, involving research areas including precision medicine, drug discovery, stem-cell biology, cellular engineering, and computational neurobiology.
Computational biology development: In 2026, RPI announced funding for computational biology projects emphasizing artificial intelligence and quantum computing, supporting faculty and student research connected with the future of biopharma and life sciences.
Accreditation value: Rensselaer has been institutionally accredited by the Middle States Commission on Higher Education (MSCHE) since 1927. The university states that this accreditation supports institutional quality assurance and eligibility for federal financial aid and research funding.
Graduation outcomes: Recent RPI undergraduate research activity has included Computational Biology students presenting work in areas such as Alzheimer’s disease, harmful algae blooms, and other interdisciplinary scientific topics. A 2026 Computational Biology graduate also received an NSF Graduate Research Fellowship for postgraduate STEM study.

Further Academic Progression: After completing the B.S. in Computational Biology, students can continue into master’s or Ph.D. programs in computational biology, bioinformatics, biology, biotechnology, data science, or related fields. Rensselaer specifically encourages graduates to pursue the expanding range of computational biology graduate programs worldwide, while its undergraduate offerings also include accelerated pathways such as the B.S. + M.S. in Biology and an Accelerated B.S./Ph.D. Program.