Are you interested in using your physics, math or statistics, computer science, chemistry or engineering background to study biology?
Introducing Computational Biology in Molecular Genetics (CBMG) - MSc/PhD
A graduate pathway for students who want to apply quantitative approaches to biological research and build expertise in computational biology and AI driven research.
Molecular genetics increasingly relies on data-rich and computational approaches, creating a strong demand for quantitative skills. Computational biologists model biological systems and analyze genome-scale data to support modern genetic research.
Who this program is for
CBMG is designed for students who
- have training in quantitative disciplines such as physics, mathematics, statistics, computer science, chemistry, or engineering
- are comfortable with programming and want to apply these skills to biological research
- are interested in machine learning, deep learning, and artificial intelligence in a biological setting
Biology students with strong programming skills and an interest in computational work are also encouraged to apply.
Quick facts
- Program
MSc or PhD - Start
September entry - Department
Molecular Genetics - Focus
Computational Biology
Program Highlights
Our faculty have labs in the Medical Sciences Building, Donnelly Centre, Hospital for Sick Children, Mt. Sinai Hospital, and the MaRS Centre — all within a few minutes' walk.
Students are guaranteed a stipend. The stipend does not require spending time as a Teaching Assistant, although those opportunities exist for those interested.
Graduate students rotate through three labs over five weeks before selecting their research group. MSc and MD/PhD students may join a PhD lab directly only with prior approval from the prospective supervisor.
Training and research environment
Advanced Computational and AI Courses
CBMG students complete advanced courses in computational biology, statistics, machine learning and AI. Topics include generative modelling, network and graph-based analysis, and methods for interpreting large-scale genomics data.
Data-driven research environment
Research across the department relies on large datasets, computational tools, and AI-driven analysis. Students contribute directly to projects that study genes, genomes, proteins, and biological systems at scale.
Accelerated Biology Course
We understand that applying quantitative skills in a biological setting takes preparation. All students begin the program with an accelerated molecular genetics course that introduces core concepts and techniques used throughout modern genetic research.
Mentorship from world-class faculty
Students work in a data-driven research environment alongside faculty who use advanced computational and bioinformatic methods. Through close mentorship, students build models, analyze large datasets, and contribute to research that advances our understanding of genes and genomes.
Admission Requirements
Application deadlines
| Deadline | Domestic | International |
| November 15 | ✔ | ✔ |
| January 15 | ✔ | ✔ |
| Post-Jan 15 rolling admissions |
✔ |
Applications to our program for the following Fall term (September start) are accepted with November 15 and January 15 deadlines. After January 15, applications from domestic applicants may still be accepted on a rolling basis, depending on the availability of remaining spots in the incoming cohort.
When all spots are filled, the admissions portal will be closed. The portal will close on May 1 at the latest.
Application Requirements
Successful applicants are admitted to the Molecular Genetics MSc or PhD program and complete coursework and research experiences that support training in computational biology. Additional materials may be required for international applicants.
Eligibility requirements include
-
Bachelor’s degree in life sciences or a quantitative discipline such as physics, mathematics or statistics, computer science, chemistry, or engineering.
-
Undergraduate average of A minus or higher, or equivalent.
-
Completed online application indicating CBMG in the Proposed Area of Study.
-
Letter of intent explaining interest in the CBMG track.
-
At least two letters of reference.
-
Successful interview.