Application Period: Oct. 2, 2017 - Oct. 31, 2017
Contact: Please submit CVs to the following address: firstname.lastname@example.org
Myko (URL http://www.myko.org) is looking for a graduate student to build predictive models for urban sustainability. In particular, we are interested in modeling the transportation choices that individuals make, and why they make them. The student should be interested in data science, have programming experience, and a familiarity with statistical modeling (i.e., fitting models to data). The exact language is flexible, but expertise in R or C++ would be an asset. Masters or Ph.D. level students are preferred, though all applications will be reviewed.
Myko is an initiative stemming from McGill’s Faculty of Law. It aims to augment environmental awareness and increase the frequency of environmentally friendly behavior through the gamification of individual daily consumption and activities. Data collection and reporting contributes to an aggregate social impact score, which is presented in turn to the user-base as a collective measure of community impacts.
We are looking for a highly-motivated student with skills not only in development but also in communication and planning. Myko is managed by a small, tight-knit team in which creativity and openness to new ideas are strongly valued. The ability to work both in groups and independently is also critical.
• Assist Professor Brian Leung in creating predictive models.
• Research and propose solutions to transportation-related models and collection of information.
• Respond to feedback from executive team.
• Attend team meetings and contribute to big-picture discussions
• Knowledge of R or C++.
• Excellent communication and organizational skills, self-motivated work ethic, intrinsic need to produce quality output, and desire to gain the most from this experience are critical to success in this role.
• The ability to work within a diversity-friendly work environment is required.
• Motivation and a dedication to the ideals Myko represents.
Computer Science is preferred but capable candidates with alternative backgrounds will be considered.