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The Department of Computing Science (http://www.cs.ualberta.ca) is home to nearly 50 tenured and tenure-track faculty members and over 200 graduate students in its PhD and thesis-based MSc programs.
The University of Alberta (http://www.ualberta.ca) ranks consistently within Canada’s five best medical/doctoral universities. Over 31,000 undergraduate students, 7,600 graduate students, and 600 postdoctoral fellows are part of the university’s exceptional learning and research environment. Edmonton (http://www.edmonton.ca), Alberta’s capital, offers a rich array of cultural, professional, sports, and entrepreneurial activities, with easy access to Canada’s Rocky Mountains, and its public elementary and secondary schools provide high-quality education.
Edmonton is also home to the Alberta Machine Intelligence Institute (Amii), which is part of the CIFAR $125M Pan-Canadian AI Strategy, as well as on-going significant provincial funding (over the last 18 years). Amii has attracted an extremely strong cohort of professors, post-docs and graduate students, and enhanced what was already a very strong machine learning community in Edmonton.
The Department of Computing Science, and Amii, at the University of Alberta has an immediate opening for one or two postdoctoral fellows to work with Professors Russ Greiner and Nidhi Hegde, to use machine learning techniques to produce models that can accurately forecast the rate of Covid-19 infection, at a population (“city”) level. The appointment will be for one year and may be renewable for an additional year subject to funding availability and satisfactory performance.
The postdoctoral fellow will work in a multi-disciplinary group composed of computer scientists, public health researchers, clinicians, and epidemiologists. Research work will include:
-Designing and building predictive tools for predicting infection and hospitalization rates due to Covid-19 several days and weeks into the future;
-Curation of datasets for use in the models;
-Building and using various types of models, including temporal probabilistic graphical modelling (e.g., extensions to HMMs) deep learning / LSTM models, as well as more standard learning models.
As the project work will involve interacting with researchers of many diverse disciplines, the candidate must be able to understand various systems and extract essential elements for modelling. The candidate must have demonstrated expertise in design and implementation of Machine Learning (including Deep Learning or Probabilistic Graphical Models), dataset management and visualisation. Familiarity with machine learning for time-varying dynamical systems will be an asset, as will knowledge of epidemiology.
The University of Alberta is committed to an equitable, diverse, and an inclusive workforce. We welcome applications from all qualified persons. We encourage women; First Nations, Métis and Inuit persons; members of visible minority groups; persons with disabilities; persons of any sexual orientation or gender identity and expression; and all those who may contribute to the further diversification of ideas and the University to apply.