The Master of Science in Bioengineering curriculum includes core courses in advanced mathematics and statistics, bioengineering, and biomolecular technology, as well as technical electives chosen from numerous engineering and life science courses. The curriculum is designed to provide flexibility and support for a student's research specialty. M.S. students are involved in the design and regulatory approval of advanced medical technologies, as well as the manufacturing of health care products. Each student's research is guided by an advisor and contributes to the knowledge base in the scientific community that forms the basis of the student's thesis. Funding opportunities are available for M.S. students.
Strengths of the program include:
- Research leading to major advances in a health care field
- Nationally and internationally recognized faculty from over a dozen departments
- Coverage of regulatory issues and approval processes with animal and human subjects
- Conducting research in state-of-the-art facilities, including the nationally renowned Veterinary Teaching Hospital
- Community of innovators on the cutting edge of research in cancer, orthopaedics, cardiovascular diseases, nanotechnology, biosensors, and more
Intra-University in Colleges of Health and Human Sciences, Engineering, Natural Sciences, Veterinary Medicine & Biomedical Sciences
Effective Fall 2021
| Code | Title | Credits |
|---|---|---|
| Core Course Requirements | ||
| BIOM 533/CIVE 533 | Biomolecular Tools for Engineers | 3 |
| BIOM 570/MECH 570 | Bioengineering | 3 |
| BIOM 576/MECH 576 | Quantitative Systems Physiology | 4 |
| BIOM 592 | Seminar 1 | 2 |
| BIOM 699 | Thesis | 8 |
| Select three credits from the following: | 3 | |
| Mathematics for Scientists and Engineers | ||
| Foundations of Applied Mathematics | ||
| Partial Differential Equations I | ||
| Numerical Methods in Science and Engineering | ||
| Linear Algebra | ||
| Linear Algebra for Data Science: Matrices and Vectors Spaces | ||
| Linear Algebra for Data Science: Geometric Techniques for Data Reduction | ||
| Linear Algebra for Data Science: Matrix Factorizations and Transformations | ||
| Linear Algebra for Data Science: Theoretical Foundations | ||
| Select four credits from the following: | 4 | |
| Data Wrangling/Visualization for Researchers | ||
| Multivariate Analysis for Researchers | ||
| Design and Data Analysis for Researchers II | ||
| Regression Models for Researchers | ||
| Experimental Design/Analysis for Researchers | ||
| Generalized Regression Models for Researchers | ||
| Mixed Models for Researchers | ||
| Machine Learning for Researchers | ||
| Electives 2 | 3 | |
| Program Total Credits: | 30 | |
A minimum of 30 credits are required to complete this program.3
- 1
BIOM 592 must be taken in two semesters.
- 2
Select a minimum of 3 credits of Engineering courses 500-level or above with approval of advisor.
- 3
Program Total Credits must include a minimum of 21 semester credits earned at CSU (not including thesis or independent study) in 500-level (or above) regular courses.