Candidates are expected to help formulate their research program within the context of related projects. Successful candidates will be supervised by Prof. Huaxiong Huang, Director of the Lab, and Prof. Arvind Gupta, both affiliated with the Department of Computer Science, University of Toronto (http://www.cs.utoronto.ca/). Huaxiong Huang is also a Professor in Applied Mathematics at York University (http://www.math.yorku.ca/~hhuang/). Collaboration with other investigators working on public health, mathematical modeling, computation, and data analysis will be critical to success. Specific tasks will include: - Develop mathematical and computational models using differential equations and machine learning approaches. - Apply modeling, simulation, and machine learning techniques to evaluate complex interventions in areas of acute and chronic disease management, diagnostics, and healthcare practices. - Integrate research ideas into applied projects to demonstrate benefit to society. - Be the subject matter expert and consult client personnel for realizing research into practice. - Articulate and present research results to clients (internal and external). Share ideas and collaborate with other members of the research lab. - Assist in developing grant proposals in related areas in collaboration with supervisors and other collaborators for NSERC/CIHR, MITAC and related competitions. - Contribute to teaching, mentorship, and supervision of postgraduate students. - Publish papers in leading academic journals. Minimum Degree Required: Ph.D.

Preferred Qualifications: A recent Ph.D. (within the past five years), preferably in Mathematics, Statistics, Computer Science, Engineering, Public Health or a related field. The candidate should also have significant experience with mathematical modeling, data analysis and computer simulation. Solid skills in at least in one programming language (Python, R, Julia, MATLAB, or C/C++); strong ability to communicate scientific material; high enthusiasm, strong work ethic, and willingness to perform at a high level consistently. Positions are available for a one-year and multi-year duration, depending on specific projects. Positions may be renewable subject to availability of funding. Salaries will be offered based on Fields Institute/University of Toronto’s policy and adjusted according to candidate’s skills and experience.

Project summaries:

Our lab collaborates with industry partners on multiple projects at any given time. Below is a brief description of our upcoming projects.

Project #1: Using a variety of consumer transaction data from grocery stores, supermarkets, and pharmacies, determine lifestyle patterns, trends, and relationships. The objective is to devise a model that can benefit consumers in making healthy lifestyle choices. The project involves analysis of multi-feature data from multiple sources. It requires the development of machine learning models that could be used to make such recommender systems. The candidate will be responsible for carrying out the research in collaboration with our industry partner in the development of the model and testing the performance of the system.

Project #2. In this project we develop efficient algorithms consisting of modeling reduction and machine learning for optimizing manufacturing processes or finding favorable conditions. Friction stir welding is a new method to fuse metals that are otherwise difficult to join. The parameterization of the process requires complex computer simulations and human intuition. We propose to simplify the process by developing a mathematical model to optimize the friction stir welding process. Furthermore, we are interested in exploring how these methods may be applied to problems in other domains such as health, energy materials, and/or nanophotonics.

The Fields Institute is a centre for mathematical research activity - a place where mathematicians from Canada and abroad, from academia, business, industry and financial institutions, can come together to carry out research and formulate problems of mutual interest. Our mission is to provide a supportive and stimulating environment for mathematics innovation and education. Learn more about us.