The Department of Biochemistry, Microbiology and Immunology at University of Ottawa, and Ottawa Hospital Research Institute (OHRI) is seeking a computational genomics postdoc follow for a full-time position applying and developing dynamic modelling or machine learning techniques to study stem cell growing, expansion and differentiation at different therapy conditions for muscle degenerative diseases. 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 stem cell biologists from the regenerative medicine program at OHRI. The position is offered for an initial two (2) years with possibility for extension and is available immediately.
The successful candidate will be highly motivated, independent and collaborative. The applicant will lead the data analysis and computational modelling through consulting with supervisors and other AI experts at NRC. The applicant will also work closely with wet-lab colleagues to test and validate the developed models on in-house experimental data. 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 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 computational modelling development, knowledge of machine learning and deep learning (particularly graph convolutional networks) is an advantage
Strong publication record with evidence for writing scientific manuscripts independently
Proficiency in programming (particularly in python/R)
Experience with analysis of genomics data sets (preferably in single cell RNA-seq)
Good spoken and written communication skills in English
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).