Practicum Student – AI-driven respiratory virus forecasting

Practicum Student – AI-driven respiratory virus forecasting Department: Scientist, Third Party Grants This posting is to fill a current vacancy. Overview We are looking for a student interested in infectious disease epidemiology and machine learning methods who is seeking an opportunity to develop their epidemiologic and analytic skills. This placement focuses on advancing machine learning methods for short-term (nowcasting) surveillance of SARS-CoV-2, influenza, and RSV activity in Ontario. The student will contribute to the development and evaluation of algorithms such as Random Forest and Extreme Gradient Boosting (XGBoost) using time series cross-validation to assess predictive performance. The project will also explore ensemble forecasting strategies that intelligently combine outputs from multiple models to balance sensitivity and stability. By improving the precision and reliability of respiratory virus forecasts, this initiative will enhance public health situational awareness and support timely, evidence-informed decisions in outbreak response, resource allocation, and health system planning. Working with Scientists and staff from the Communicable Disease Control, Communicable Diseases, and Data Science and Analytics teams, the student will have the opportunity to develop analytic plans and undertake statistical analyses. The student may also contribute to the preparation of a report or manuscript. This practicum is an ideal fit for a student with strong statistical analysis skills who wishes to gain experience in infectious disease epidemiology. The student will work as part of a multidisciplinary research team to conduct the work and will have the opportunity to learn more about the datasets available at PHO and how they can be used to contribute to public health research. Suggested placement start date: May to August 2026 Responsibilities Develop and apply analytical plans and undertake statistical analyses in collaboration with the scientific and data teams. Contribute to the development and evaluation of machine learning methods for nowcasting of respiratory viruses. Explore ensemble forecasting strategies that combine outputs from multiple models to improve precision and reliability. Prepare materials such as reports or manuscripts as part of the project deliverables. Qualifications Currently enrolled in a Master’s degree in biostatistics, epidemiology, computer science, data science, or similar where the completion of a practicum/internship placement is required. Good knowledge of R for data manipulation and analysis preferred; experience with another analytic software will be considered. At least a basic understanding of machine learning and forecasting methods. Familiarity with infectious disease epidemiology is an asset. Proposed Deliverables At The End Of The Placement An abstract for submission to a conference. A manuscript for submission to a peer-reviewed journal or a report delivered to the team. Educational Objectives Develop and apply analytical skills (e.g., machine learning, forecasting, ensemble models). Practice communication skills (abstract and manuscript writing, team presentations). Gain experience in public health and working with laboratory testing data. Details Duration: Student (Fixed Term), 3 month(s) Hours of Work: Full time, 36.25 hours per week Compensation Range: $700.00 - $725.00 Posting Date: 01-5-2026 Closing Date: Please note: applications will be received no later than 11:59pm on the date preceding the closing date as indicated on the Job Requisition. Note: Internal candidates will be considered first. Emphasis on equity: PHO is committed to ensuring equity in employment. Our goal is a diverse, inclusive workforce and accessible services and communications. If a candidate requires a job posting in an alternative format, please email If selected for an interview, candidates may inform PHO about any accommodations needed at any stage of the interview process. Required Education And Experience Currently enrolled in a Master’s degree in biostatistics, epidemiology, computer science, data science, or similar program where practicum/internship placement is required. Good knowledge of R for data manipulation and analysis preferred, but experience with another analytic software will be considered. At least a basic understanding of machine learning and forecasting methods. Familiarity with infectious disease epidemiology is an asset. Note: Internal candidates will be considered first. Please be advised that the listing includes multiple unrelated postings in some sections; this refined version includes the Practicum Student – AI-driven respiratory virus forecasting role only. #J-18808-Ljbffr