Our lab develops machine learning techniques to better understand chromatin biology. These models and algorithms transform high-dimensional functional genomics data into interpretable patterns and lead to new biological insight. A key focus of the lab is to train a new generation of computational biologists.
We seek postdoctoral fellows for several projects in computational genomics and machine learning. Selected projects include:
- Integrating epigenomic and sequence data to better understand human gene regulation.
- Developing deep learning techniques to find novel behavior in multiple functional genomics datasets.
- Creating models of transcription factor binding that allow us to predict the effects of perturbations.
The Princess Margaret Cancer Centre is a teaching hospital affiliated with the University of Toronto. It has the largest cancer research program in Canada. It is a component of the University Health Network, Canada's largest hospital.
The Hoffman Lab is part of a Centre for Cancer Epigenomics at the Princess Margaret. There are ample opportunities for collaboration and scientific exchange with a large community of genomics, computational biology, and machine learning groups at the University of Toronto and associated institutions.
Required qualifications: Doctorate in computational biology, computer science, electrical engineering, statistics, or physics. Submitted papers in genomics or machine learning research. Expertise in Python and Unix environments.
Preferred qualifications: Experience with epigenomics and graphical models. Published papers in peer-reviewed journals or refereed conference proceedings. Expertise in R, C, and C++.
To apply: We will accept applications until the position is filled. Please submit a CV, a PDF of your best paper, and the names, email addresses, and phone numbers of three references to the address at our recruitment web page.