Center for Addiction and Mental Health



, Ontario


Job Type:


Degree Level Required:


418 days ago Apply now

Join our team as we integrate large-scale, genomic data and computational approaches to understand and predict psychiatric outcomes in clinical-treatment cohorts!

Applications are invited for a Postdoctoral Fellow position at the Pharmacogenetics Research Clinic at the Centre for Addiction and Mental Health and the University of Toronto, under the joint supervision of Dr. Daniel Mueller (M.D., Ph.D.) and Dr. Daniel Felsky (Ph.D.). The postdoctoral fellow will integrate psychiatric pharmacogenetics with various methods in computational biology and bioinformatics. Our primary goal is to understand and optimize the methodological opportunities and challenges for machine learning associated with the integration of genome-wide data from large-scale, epidemiological datasets and smaller, clinical-treatment cohorts. The postdoctoral fellow will engage in projects that aim to understand the genetic contributions to antidepressant non-remission in adults with depression and other psychiatric disorders within the context of underlying pathophysiology associated with aging and cerebrovascular and neurodegenerative changes.


The postdoctoral fellow will be involved in research projects involving genomics and various computational approaches, including predictive modelling and critical evaluation. The postdoctoral fellow will be integrally involved in developing open-source computational pipelines for analyzing large-scale genomic data and predictive modelling for complex outcomes. In-depth knowledge of bioinformatics and various computation methods will be an asset, as well as documented experience with in silico characterization of non-coding genetic variation and developing computational pipelines. In addition, the postdoctoral fellow will be open to learning new cutting-edge methods and techniques as research opportunities develop.

Given the importance of scientific communication, the ideal candidate will have excellent data visualization skills and experience in using tools for reproducible research (R notebooks, git). The postdoctoral fellow should have strong written and oral communication skills, as demonstrated by peer-reviewed publications and conference presentations and have the ability to work under deadlines independently or with general guidance. The collaborative nature of our research will require efficient communication with international collaborators who may be physicians, biologists, statisticians, and computer scientists. Lastly, our labs support the training and mentorship of young trainees; therefore, the postdoctoral fellow will be expected to assist in training new trainees.



  • Ph.D. (or similar qualification) in Bioinformatics, Biostatistics, Computer Science, computational sciences, relevant field, or a combination of relevant experience and education.
  • Must have knowledge and experience of statistical genetics or genetic epidemiology for working with genome-wide genetic data (genotype and other variation) for complex trait analyses.
  • Must have proficient computational skills, including a high-proficiency in R, using UNIX-like OS, and high-performance computing.
  • Experience with computational methods development and application (e.g., developing R packages)
  • Experience with code management and version control, including git.
  • Experience with machine learning techniques, such as random forest models, support vector machines, and other supervised or unsupervised approaches.
  • Ability to work independently and collaboratively as a member of an interdisciplinary team.
  • Ability to multitask and manage projects
  • Strong verbal and written communication skills


  • Experience managing large and complex datasets
  • Experience with supervising students, mentoring, or teaching
Additional Information:


Applicants are asked to provide the following items in their application package:

  • A cover letter stating their research experience and specific interest in our lab;
  • A CV including a clear list of peer-reviewed publications include journal impact factors (IF), as well as a 1-2 sentence description of their contribution to the publication;
  • A brief description (up to 250) of how the applicant approaches challenges and conflict resolution in research;
  • A list of skills and proficiencies in pertinent computational applications, as well as the level of proficiency; and,
  • Contact information of three references, including name, email, and relationship to the applicant.

Please send the application package to Daniel Mueller ( and CC Samar Elsheikh (



Machine Learning


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