Program Overview

Ph.D in Bioengineering

The Ph.D. in Bioengineering program is designed to produce graduates who demonstrate technical mastery of the bioengineering disciplines, advance the theory and practice of bioengineering, and maintain high standards of scholarly excellence. Students perform original research guided by an advisor and contribute to the knowledge base in the scientific community.

Program Overview

The program includes core courses in advanced mathematics and statistics, biomedical engineering, and biotechnology, as well as technical electives chosen from numerous engineering and life science courses. The curriculum is designed to provide flexibility and support the chosen research specialty.

Learning Objectives

The Ph.D. program in bioengineering aims to produce graduates who:

1. Demonstrate technical mastery of the bioengineering disciplines of advanced engineering mathematics, biomolecular tools, bioengineering, physiology, and statistics.
2. Advance the theory and practice of bioengineering by making original research contributions that are both novel and significant.
3. Maintain high standards of scholarly excellence and responsible research conduct.
4. Demonstrate competency at assimilating information from other related fields of science and engineering to inform their intellectual pursuits and to expand the areas of application of their bioengineering expertise.
5. Effectively and professionally disseminate their research in the primary peer-reviewed and patent literature, and through technical conferences and symposia.

Course Requirements

• Core Course Requirements:
  • BIOM 533/CIVE 533: Biomolecular Tools for Engineers (3 credits)
  • BIOM 570/MECH 570: Bioengineering (3 credits)
  • BIOM 576/MECH 576: Quantitative Systems Physiology (4 credits)
  • BIOM 592: Seminar 1 (4 credits)
  • BIOM 799: Dissertation (15-18 credits)
• Select three credits from the following:
  • MATH 530: Mathematics for Scientists and Engineers
  • MATH 535: Foundations of Applied Mathematics
  • MATH 545: Partial Differential Equations I
  • MATH 550/ENGR 550: Numerical Methods in Science and Engineering
  • MATH 560: Linear Algebra
  • MATH 569A/DSCI 569A: Linear Algebra for Data Science: Matrices and Vector Spaces
  • MATH 569B/DSCI 569B: Linear Algebra for Data Science: Geometric Techniques for Data Reduction
  • MATH 569C/DSCI 569C: Linear Algebra for Data Science: Matrix Factorizations and Transformations
  • MATH 569D/DSCI 569D: Linear Algebra for Data Science: Theoretical Foundations
• Select four credits from the following:
  • STAR 501: Data Wrangling/Visualization for Researchers
  • STAR 502: Multivariate Analysis for Researchers
  • STAR 512: Design and Data Analysis for Researchers II
  • STAR 513: Regression Models for Researchers
  • STAR 514: Experimental Design/Analysis for Researchers
  • STAR 531: Generalized Regression Models for Researchers
  • STAR 532: Mixed Models for Researchers
  • STAR 534: Machine Learning for Researchers

Program Requirements

• A minimum of 72 credits are required to complete this program.
• Program Total Credits must include a minimum of 42 semester credits earned at CSU (while in the graduate program), a minimum of 32 semester credits earned after admission to CSU, and a minimum of 12 semester credits earned at CSU (post-master's degree) in 500-level or above courses (not including dissertation and independent study).
• Completion of the Ph.D. also requires successfully completing a qualifying exam, a preliminary exam, and a dissertation defense.

Strengths of the Program

• Opportunities to develop major advances in the health care field.
• Nationally and internationally recognized faculty from over a dozen departments.
• Practical and academic experience with 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, orthopedics, cardiovascular disease, nanotechnology, biosensors, and more.