Thank you for your interest in joining AIMM Lab. We are actively looking for passionate postdocs to join our Lab. We are especially keen on recruiting postdocs with strong quantitative abilities and with a deep motivation to tackle novel challenges in health, ecology, and environmental management. If  our scientific interests overlap, and you will like to join us,  please contact us via jude.kong@utoronto.ca. In your email, please include the following:

  • “AIMM lab Postdoc Application” in the email subject.
  • Attach a cover letter addressing reasons and motivation for postdoc work.  In the letter, indicate which existing AIMM Lab research projects you feel like you would fit with best. Also, if you have a related project that you are particularly interested in pursuing, please outline it.
  • Attach your CV.
  • Attach up to 3 samples of your writing (e.g., reports, theses, papers, etc.).

All postdocs are welcome here regardless of race, religion, gender identification, sexual orientation, age, or disability status.

Essential qualities for successful applicants are:

1. A significant drive to effect change in challenges within health, ecology, or environmental management.
2. Proficiency in mathematics equivalent to, or surpassing, a minor specialization in the subject during undergraduate studies.
3. Proficient programming abilities, such as in Python, R, or related languages.
4. Excellent communication skills encompassing both spoken and written forms.

Some areas to work on in our lab include, but not limited to: applying artificial intelligence to develop advanced visualization and analytics tools that will assist policy makers; now-casting and forecasting of COVID-19 pandemic waves to inform healthcare planning; determine initial disease spread characteristics within communities prior to use of non-pharmaceutical interventions (NPI); employ mathematical models to examine NPI effectiveness, for example, the effect of a home quarantine policy, social distancing interventions, tracing apps, testing and isolation on the epidemic development; determine NPI effects on healthcare demand; determine the effects of vaccination on the NPI uptake needed for post- vaccine waves of infection; develop an overarching economic-epidemiological model for scientists and policy-makers; build spatial disease spread models and simulations for the outbreak of COVID-19 at a localized level to model the effects of informal settlements and varying social distancing practicalities in townships, performance of hotspot analysis, and the identification of vulnerable areas; analyses of outbreak data in specific settings (e.g. households, hospitals); mathematical/statistical modelling of the COVID-19 pandemic.