Computational modelling for Stem Cell Signaling and Dynamics

Ottawa Hospital Research Institute / University of Ottawa & National Research Council
Regenerative Medicine / Biochemistry, Microbiology & Immunology
Canada Ontario Ottawa


The OHRI and NRC is seeking a computational biology postdoc fellow for applying and developing machine learning techniques to study stem cell signaling and dynamics for muscle degenerative diseases.

The Department of Biochemistry, Microbiology and Immunology at University of Ottawa, and Ottawa Hospital Research Institute (OHRI) is seeking a machine learning and computational biology postdoc follow for a full-time position applying and developing machine leanring/deep learning techniques to study stem cell growing, expansion and differentiation for muscle degenerative diseases. The goal of this project is to develop AI-driven approaches to construct predictive models of stem cell systems that will enable one to measure the muscle stem cell states and decisions. The position is part of the recently funded project “AI powered design of stem cell therapy for degenerative muscle diseases” selected through the National Research Council Canada (NRC) AI for Design Challenge Program. This is a multidisciplinary project led by Dr. Theodore J. Perkins (the leader of the Machine Learning in Genomics group, OHRI) and Dr. Xiaojian Shao (Research officer at NRC) and involves collaborations with other teams from NRC and the regenerative medicine program at OHRI. The position is offered for an initial two (2) years with possibility for extension and is available as early as March 2021.

The successful candidate will be highly motivated, independent and collaborative. Applicants are required to have experience in machine learning/deep learning and computational biology with programming skills (particularly Python, R & bash). Previous experiences in computational genomics with the analysis of large-scale genomics data particularly in single cell RNA-seq data with a strong publication record is a plus. We offer a highly stimulating environment with state-of-the-art infrastructure including sophisticated HPC environment, and a competitive salary commensurate with the qualifications and experience of the candidate as well as an excellent benefits package.

- Analyze publicly available and in-house multi-omics data, particularly on single cell RNA-seq datasets
- Apply/develop novel machine learning algorithms for predicting stem cell state of maturity and cell-cell interaction/communications involved in the muscle stem cell regeneration
- Collaborate with both wet-lab experts and dry-lab researchers
- Conduct high-quality research, write peer-review articles and present research at national and international conferences and meetings


PhD in computational biology, computer science, applied mathematics, genetics or a related field

Documented experience with machine learning model development, knowledge of deep learning (particularly graph convolutional networks) is an advantage

Strong publication records with evidence for writing scientific manuscripts independently

Proficiency in programming (particularly in python)

Experience with analysis of genomics data sets (particularly in single cell RNA-seq)

Good spoken and written communication skills in English

Start date

As soon as possible

How to Apply

Please send in one PDF that contains a cover letter outlining motivations, career goals, and brief description of research experience and interests, as well as a CV with list of publications and relevant techniques and contact information for three references (name, relation to candidate, email and phone number).

emails: and


Dr. Theodore Perkins

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