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A postdoctoral fellowship is available for one year (with renewal for a 2nd year if warranted) in a collaboration with several research laboratories including the Center for Optimization, Convex Analysis, and Nonsmooth Analysis (COCANA) at UBC Okanagan. The postdoctoral fellow will join a team of researchers on a project with the BC Oil and Gas Commission to explore machine learning techniques for process improvement. 

Oil and gas operators must apply to the provincial regulator authorities (the BC Oil and Gas Commission) for approvals before beginning work; examples of considerations for approval include the impact on the environment, Indigenous Nations, forestry, archaeology, etc. Efficacy of the Commission is critical for the economic dynamism of the sector. In 2016, the Commission implemented a massive change to their application processes that allowed oil and gas operators to bundle multiple activities (e.g. wells, facilities, pipelines, roads, etc.) into a single application and obtain a decision for the entire application instead of multiple independent decisions for each planned activity. While the new application process brings efficiencies for the applicant, no two applications are the same (as the number and type of activities can vary), rendering data analysis more complex. To improve services for BCOGC stakeholders, an NSERC Alliance grant was awarded to fund a postdoctoral fellow position and benchmark both application processing timelines and resource forecasting.

The project will explore the variables that may increase the complexity of an application and its timeline, as well as forecasting application volume in prevision of resources management. Using several years of data available on application activities, predictive models will be built. The project’s challenge is to balance models accuracy with explainability, interpretability, and fairness in a sensitive environment that includes oil and gas companies, environmental activities, and Indigenous lands. Addressing such challenges requires new predictive models running on an efficient computer architecture. Thus, the project seeks to:

• Identify key factors that impact application timeline, and the parameters that triggers application volume.

• Build machine learning models to predict application timelines based on 5-6 years of data, and to predict the volume of application on a monthly basis.

• Compare the models for accuracy, robustness, fairness, interpretability, and explainability.

• Build a resource model based on predictions on application levels.

Models under consideration include deep neural networks, optimal classification trees, optimal rule lists, and generative additive models.

The successful candidate will have completed, or be nearing completion, of a Ph.D. in Computer Science, Engineering, Mathematics, Statistics, or a related discipline. They must have previous experience in developing machine learning models and implementing them successfully (knowledge of business process mining is an asset). In addition, as this is a time sensitive client project, the following soft skills are desirable experience that should be highlighted within the application letter: communication skills with industrial collaborators, project management skill, supervision and mentoring of undergraduate and master’s students, and any successful experience leading projects.

To apply, submit a Cover Letter and CV to

The cover letter should clearly indicate, and justify, which areas of experience are applicable. In addition, arrange for 3 reference letters to be sent directly to the above.

This posting is for the UBC Okanagan campus in Kelowna, British Columbia, Canada. Please refer to reference number HS-55848 during correspondence about this position. Equity and diversity are essential to academic excellence. An open and diverse community fosters the inclusion of voices that have been underrepresented or discouraged. We encourage applications from members of groups that have been marginalized on any grounds enumerated under the B.C. Human Rights Code, including sex, sexual orientation, gender identity or expression, racialization, disability, political belief, religion, marital or family status, age, and/or status as a First Nation, Metis, Inuit, or Indigenous person.