All CBB PhD students are required to serve as teaching assistants or fellows for two assignments (one per term) regardless of level during their training period. It is strongly recommended that students wait until the 4th year (after qualifying) to complete these appointments.
Teaching provides the student the opportunity to develop teaching skills under the guidance of faculty. Attendance at all classes and discussion sessions is essential. On average, PhD students should expect to spend no more than 10 hours per week on teaching and assignments (including the usual expectation of grading exams). TAs and faculty should remain clear on what is expected of their assignment and it is imperative that TAs are aware of exam deadlines and make arrangements with faculty in case there should be any conflicts.
As an interdepartmental program, CBB allows teaching assignments in a number of departments. The CBB registrar maintains a list of all the courses in which CBB students have been TAs which serves as a useful starting point for finding an appointment. If students are interested in teaching outside BBS, e.g., Computer Science, Bioengineering, Statistics, etc. they should contact the registrar within each program.
In June of each year, a list of available TA opportunities in the fall and spring semesters within the BBS departments is emailed to all CBB students. Students who wish to teach in the following academic year should fill out the included online form indicating which courses they would like to TA in by the given deadline (or preferably as soon as possible as class requests fill up quickly). Students should consider contacting faculty well in advance of the selection notice to convey their interest in assisting in specific courses. Students should also notify the CBB registrar of confirmed teaching positions.
Examples of courses in which CBB students have been TAs include the following:
- CBB 645b Statistical Methods in Genetics and Bioinformatics
- CBB 750b Core Topics in Biomedical Informatics
- CBB 752a Biomedical Data Science: Mining and Modeling
- MATH 215b Introduction to Management Science: Probabilistic Models
- MCDB 200b Molecular Biology
- STAT 538a Probability and Statistics for Scientists
- STAT 660b Multivariate Statistical Methods for the Social Sciences