The Biomedical Engineering doctoral degree requires a minimum of 72 credits beyond the bachelor's level, with a minimum of 30 being course credits (including the core curriculum) and a minimum of 24 credits of doctoral dissertation research.
The core curriculum that must be satisfied by all PhD students consists of the following:
- One graduate-level course (≥3 credits) in life science from an approved list
- One graduate-level course (≥3 credits) in mathematics from an approved list
- One graduate-level course in (≥3 credits) computer science from an approved list or exemption by proficiency
- Four BME courses (≥12 credits) from an approved list
Up to 3 credits of BME 5999 Independent Study and/or 3 credits of BME 8887 BME Doctoral Seminar Series may be counted toward the 30 credits of graduate courses required for the PhD. A total of 24 additional credits, including the core curriculum, are required for the PhD. Up to two 4000-level courses may be counted toward the PhD coursework requirements. Graduate courses may be transferred in (up to 24 credits) but must be evaluated and approved by the Director of Doctoral Studies. The evaluation and approval may occur at any time, but course transfer does not become official until after one year in residence at Washington University.
Students seeking the PhD in Biomedical Engineering enroll in two to three courses each semester. Before the end of their first 10 months of enrollment in the program, students take their oral qualifying exam, which consists of a presentation of their research done to date in the mentor's laboratory followed by an oral exam addressing any issues directly related to their qualifying exam report or their oral presentation. Upon successfully passing the qualifying examination, they advance to candidacy and complete the balance of their requirements. During the second and third years, students complete their remaining courses, participate in a mentored teaching experience, and begin their thesis research. By the end of the third year, students must complete their thesis proposal.
Courses
BME Requirement (12 credits)
| Code | Title | Units |
|---|---|---|
| BBS 5014 | Biotech Industry Innovators | 3 |
| BBS 5146 | Principles and Applications of Biological Imaging | 3 |
| BBS 5147 | Contrast Agents for Biological Imaging | 3 |
| BBS 5311 | Dynamics in Mesoscopic Molecular Systems | 3 |
| BBS 5312 | Macromolecular Interactions | 3 |
| BME 4710 | Bioelectric Phenomena | 3 |
| BME 4960 | Design and Development of Optical Imaging Systems | 3 |
| BME 5190 | Advanced Cognitive, Computational, and Systems Neuroscience | 3 |
| BME 5230 | Biomaterials Science | 3 |
| BME 5320 | Biomolecular Interaction Networks | 3 |
| BME 5330 | Biomedical Signal Processing | 3 |
| BME 5340 | Biophysical chemistry | 3 |
| BME 5420 | Principles of Biomolecular Spectroscopy | 3 |
| BME 5430 | Molecular and Cellular Engineering | 3 |
| BME 5440 | Biomedical Instrumentation | 3 |
| BME 5501 | Translational Neuroengineering | 3 |
| BME 5590 | Intermediate Biomechanics | 3 |
| BME 5642 | Human-Machine Interfaces | 3 |
| BME 5650 | Biosolid Mechanics | 3 |
| BME 5690 | Cardiac Electrophysiology | 3 |
| BME 5720 | Biological Neural Computation | 3 |
| BME 5744 | Open Challenges in Systems Neuroscience | 3 |
| BME 5750 | Molecular Basis of Bioelectrical Excitation | 3 |
| BME 5771 | Biomedical Product Development | 3 |
| BME 5780 | Engineering for Women's Health | 3 |
| BME 5790 | Biofabrication & Medical Devices | 3 |
| BME 5901 | Integrative Cardiac Electrophysiology | 3 |
| BME 5910 | Biomedical Optics I: Principles | 3 |
| BME 5920 | Biomedical Optics II: Imaging | 3 |
| BME 5940 | Ultrasound Imaging | 3 |
| BME 5950 | Drug Delivery Systems: Principles and Applications | 3 |
| CHEM 5680 | Special Topics in Inorganic Chemistry (Interface Science forSoft Materials and Biomedicine) | 3 |
| EECE 5190 | Molecular Biochemical Engineering | 3 |
| ESE 4380 | Applied Optics | 3 |
| ESE 4480 | Control Systems Design Laboratory | 3 |
| ESE 4820 | Digital Signal Processing | 3 |
| ESE 5460 | Dynamics & Control in Neuroscience & Brain Medicine | 3 |
| ESE 5820 | Fundamentals and Applications of Modern Optical Imaging | 3 |
| ESE 5890 | Biological Imaging Technology | 3 |
| MEDPHYS 5010 | Radiological Physics and Dosimetry | 3 |
| MEDPHYS 5060 | Radiobiology | 2 |
| MEDPHYS 5070 | Radiation Oncology Physics | 3 |
| MEDPHYS 5080 | Radiation Protection and Safety | 2 |
| MEMS 5562 | Cardiovascular Mechanics | 3 |
| MEMS 5565 | Mechanobiology of Cells and Matrices | 3 |
| MEMS 5566 | Engineering Mechanobiology | 3 |
| MEMS 5606 | Soft Nanomaterials | 3 |
| MEMS 5607 | Introduction to Polymer Blends and Composites | 3 |
| MEMS 5608 | Introduction to Polymer Science and Engineering | 3 |
| MEMS 5613 | Biomaterials Processing | 3 |
| MEMS 5614 | Polymeric Materials Synthesis and Modification | 3 |
| MEMS 5910 | Biomechanics Journal Club | 1 |
| PSYCH 4450 | Functional Neuroimaging Methods | 3 |
Life Science (3 credits)
| Code | Title | Units |
|---|---|---|
| ANTHRO 4581 | Principles of Human Anatomy and Development | 3 |
| ANTHRO 4598 | Biomarkers: Measuring Population Health, Reproductive, and Social Endocrinology | 3 |
| BBS 3532 | Developmental Biology | 3 |
| BBS 4071 | Developmental Biology | |
| BBS 5053 | Immunobiology I | 4 |
| BBS 5068 | Fundamentals of Molecular Cell Biology | 4 |
| BBS 5224 | Molecular, Cell and Organ Systems | 3 |
| BBS 5285 | Current Topics in Human and Mammalian Genetics | 3 |
| BBS 5319 | Molecular Foundations of Medicine | 3 |
| BBS 5357 | Chemistry and Physics of Biomolecules | 3 |
| BBS 5392 | Molecular Microbiology & Pathogenesis | 4 |
| BBS 5480 | Nucleic Acids & Protein Biosynthesis | 3 |
| BBS 5488 | Genomics | 4 |
| BBS 5501 | Biology of the Visual System | 3 |
| BBS 5571 | Cellular Neurobiology | 6 |
| BBS 5651 | Neural Systems | 6 |
| BBS 5663 | Neurobiology of Disease | 2 |
| BBS 5928 | Experimental Cancer Biology | 3 |
| BBS 5940 | Foundations in Cancer Biology and Experimental Cancer Biology | 3 |
| BIOL 4030 | Biological Clocks | 3 |
| BIOL 4040 | Laboratory of Neurophysiology | 4 |
| BIOL 4181 | Population Genetics | 3 |
| BIOL 4270 | Problem Based Learning in Biomedical Sciences | 3 |
| BIOL 4310 | Biology of Aging | 3 |
| BIOL 4381 | Cell-Based Tissue Engineering and Regenerative Medicine | 3 |
| BIOL 4510 | General Biochemistry | 4 |
| BIOL 5114 | Neuroplasticity Wiring and Rewiring of the Brain | 3 |
| BIOL 5181 | Population Genetics | 3 |
| BIOL 5241 | Immunology | 4 |
| BIOL 5309 | Biology of Aging | 3 |
| BIOL 5716 | Advanced Cancer Biology | 3 |
| BME 5300 | Molecular Cell Biology for Engineers | 3 |
| BME 5380 | Cell Signal Transduction | 3 |
| CHEM 4810 | General Biochemistry I | 3 |
| CHEM 4820 | General Biochemistry II | 3 |
| CHEM 4833 | Protein Biochemistry | 3 |
| IPMS 5611 | Movement Science III--Biocontrol Mechani | 3 |
| PHTPS 6012 | Global Reproductive Health | 3 |
| PSYCH 5631 | Introduction to Computational Cognitive Science | 3 |
| PSYCH 5665 | The Science of Behavior | 3 |
| REPRSCI 5000 | Human Reproductive Physiology | 3 |
Mathematics (3 credits)
| Code | Title | Units |
|---|---|---|
| BBS 5075 | Introduction to Coding and Statistical Thinking for Genetics and Genomics (Fundamentals of Biostatistics for Graduate Students) | 2 |
| BBS 5648 | Coding and Statistical Thinking in the Neurosciences | 3 |
| BME 5700 | Mathematics of Imaging Science | 3 |
| CLNV 5151 | Intermediate Statistics for the Health Sciences | 3 |
| EECE 5030 | Mathematical Methods in EECE | 3 |
| ESE 5010 | Mathematics of Modern Engineering I | 3 |
| ESE 5020 | Mathematics of Modern Engineering II | 3 |
| ESE 5200 | Probability and Stochastic Processes | 3 |
| MATH 4501 | Numerical Applied Mathematics | 3 |
| MATH 4540 | Partial Differential Equations | 3 |
| PHFN 5001 | Biostatistics | 3 |
| PHYSICS 5010 | Theoretical Physics | 3 |
| PHYSICS 5020 | Methods of Theoretical Physics II | 3 |
| PHYSICS 5027 | Introduction to Computational Physics | 3 |
| PHYSICS 5810 | Critical Analysis of Scientific Data (1 credit for math, 2 credits for general electives) | 3 |
| SDS 4020 | Mathematical Statistics | 3 |
| SDS 5020 | Mathematical Statistics | 3 |
| SDS 5210 | Statistical Computation | 3 |
| SDS 5480 | Topics in Statistics | 3 |
Computer Science (3 credits)
| Code | Title | Units |
|---|---|---|
| BME 4400 | Biomedical Data Science | 3 |
| BME 5401 | Biomedical Data Science | 3 |
| CSE 4102 | Introduction to Artificial Intelligence | 3 |
| CSE 4107 | Introduction to Machine Learning | 3 |
| CSE 5105 | Bayesian Methods in Machine Learning | 3 |
| CSE 5107 | Machine Learning | 3 |
| CSE 5306 | Rapid Prototype Development and Creative Programming | 3 |
| CSE 5401 | Advanced Algorithms | 3 |
| CSE 5403 | Algorithms for Nonlinear Optimization | 3 |
| CSE 5504 | Geometric Computing for Biomedicine | 3 |
| CSE 5509 | Computer Vision | 3 |
| CSE 5515 | Computational Photography | 3 |
| CSE 5804 | Algorithms for Biosequence Comparison | 3 |
| CSE 5807 | Algorithms for Computational Biology | 3 |
| ESE 4170 | Introduction to Machine Learning and Pattern Classification | 3 |
| PHEL 6005 | Applied Machine Learning Using Health Data | 3 |
Electives (9 credits)
To meet the elective requirement students may choose from BBS, BME, CHEM, CSE, EECE, ESE, MEDPHYS, MEMS, PHYSICS, and REPRSCI courses at the 4000 and 5000 levels. Courses taken from departments outside of those listed can also be allowed to apply to the elective requirement (i.e., 4000 and 5000 level) with approval from the research mentor and department. Only 6 credits of 4000-level coursework can apply to the PhD coursework requirements.
Contact Info
As part of their degree requirements, PhD students must complete a program-defined Mentored Experience Requirement (MER) as per these guidelines. The Mentored Experience Implementation Plan (MEIP) is the written articulation of a program-defined degree requirement for PhD students to engage in mentored teaching activities and/or mentored professional activities, collectively referred to as the MER.
Mentored Experience Requirement (MER)
Philosophy of Teaching
We are educating students for careers in industry, in government, and in academia with a concentration on research. Therefore, it is important that our graduates know how to convey technical knowledge in both lecture and interactive settings to a wide audience (from peers in the field to trainees with a limited understanding of the nuances of the topic). At present, our program requires the mentored teaching experience, not the mentored professional experience.
Preparatory Engagement
Preparatory Engagement activities are those that represent an introduction to the foundational skills associated with teaching or communication. Pedagogical preparation engagement activities are normally completed before students are permitted to engage in assisting or teaching in a classroom.
Two preparatory courses that have been approved by the Biomedical Engineering graduate committee are required.
Mentored Teaching Experiences (MTEs)
Assistant in Instruction (AI)
An Assistant in Instruction (AI) is a PhD student who is directly engaged in the organization, instruction, and/or support of a semester-long course primarily taught by a faculty member. An AI receives mentorship from a faculty member related to best practices in classroom engagement, instruction in the field, interpersonal engagement, and other relevant skills. Students and mentors complete a mentorship plan prior to the start of each AI experience. To complete each AI assignment and to ensure that it applies toward their degree requirements, students must register for the appropriate course number for each semester of engagement. Refer to the "Required Pathways for Completion" section below for course numbers and details.
Each student will be the AI for one course at 10 MER units by registering for EGS 8010 for the semester of engagement or for a two-semester long course at 5 MER units per semester by registering for EGS 8005 for the two semesters of engagement. Students are eligible for an AI assignment after they pass the qualifying exam and typically when they are beginning the second year of the program. Students work with their department coordinator or graduate program advisor on the timing of their AI assignments.
Required Pathways for Completion
Students work with their faculty mentor and their Director of Graduate Studies to plan how and when they will complete their MER. Students register during the normal registration period for courses in accordance with one of these approved pathways.
- Preparatory Engagement
Pathway #1
| EGS 8010 | Students register one time for the semester of the AI assignment |
Pathway #2
| EGS 8005 | Students register twice, one time for each of the AI assignment semesters |