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1 month ago
We are seeking a highly skilled Postdoctoral Fellow to join our research team at the University of Saskatchewan. The successful candidate will be responsible for developing a Machine Learning algorithm to quantify the safety of underground mine roofs and sidewalls using audio recordings of tool impacts on the surface.
Primary Responsibilities:
- Develop and optimize a Machine Learning algorithm to predict whether an audio recording is drummy (unsafe) or tight (safe)
- Collaborate with a student who is currently taking the recordings and other students working on other projects
- Produce regular progress reports to the supervisor and industrial partners
Requirements:
- Must have completed a degree in a related field within the last five years
- Demonstrated experience in developing Machine Learning algorithms with small datasets of arbitrary and/or time-series data
- Experience with dataset feature engineering and dimensionality reduction
- Ability to take the lead role in successfully published paper(s) in high-quality journals or peer-reviewed conferences
- Experience with recording and processing of audio data
- Python ML programming experience
- Familiarity with rock mechanics and mine operations
Working Environment:
The work environment will be a typical office, although visits to an underground potash mine site may be desirable to understand the background and possibly to take new data or improve the data recording process. Hours will be flexible, based around regular university hours, and working from home part-time is possible.
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