Research Associate in Mechanical Engineering
4 weeks ago
Postdoctoral Researcher in Mechanical Engineering (Machine Learning for Mining Safety)
Primary Objective: The selected candidate will engage in the creation of a Machine Learning model aimed at assessing the safety of underground mine structures through the analysis of audio data captured during tool interactions with surfaces. This initiative is part of a collaborative effort led by Prof. Travis Wiens in the Department of Mechanical Engineering, in partnership with Nutrien, a leading global potash mining company, and is supported by NSERC funding.
Work Environment: The Postdoctoral Fellow (PDF) will operate under the guidance of Prof. Wiens. A pre-existing dataset containing audio recordings of tool impacts on mine surfaces is available and will be further enhanced by another team member. These recordings are labeled as either 'drummy' (indicating potential hazards) or 'tight' (indicating safety), as determined by seasoned mining experts. The PDF will utilize contemporary machine learning techniques to develop and refine an algorithm capable of predicting the safety classification of these recordings. Additionally, the role involves extending the model to quantify the degree of risk and the reliability of these predictions. The dataset presents challenges such as limited sample sizes relative to recording durations, uncertainty in labeling, and moderate levels of background noise.
The PDF is expected to work autonomously, fostering innovation and validating new concepts while also taking a proactive role in disseminating findings through scholarly publications and presentations at conferences. Although the PDF will not formally supervise other students, they will provide mentorship and collaborate with a student responsible for data collection, as well as with peers engaged in related projects.
Accountabilities: The PDF will be required to submit regular progress updates to both their supervisor and industrial collaborators, as well as funding organizations. They will also be responsible for authoring articles for publication in esteemed peer-reviewed journals and conferences.
Qualifications: Candidates must possess a degree in a relevant field obtained within the last five years.
Licenses: Not applicable.
Experience:
- Essential Experience:
- Proven experience in developing machine learning algorithms utilizing small datasets, particularly in time-series contexts.
- Demonstrated expertise in dataset feature engineering and dimensionality reduction techniques.
- Track record of leading roles in successfully published research in high-impact journals or peer-reviewed conferences.
- Preferred Experience:
- Experience in the recording and processing of audio data.
- Familiarity with establishing confidence intervals for machine learning classifications or regression analyses.
- Proficiency in Matlab for machine learning applications.
- Proficiency in Python for machine learning applications.
- Knowledge of rock mechanics and mining operations.
- Understanding of acoustic and vibrational wave propagation principles.
Skills: Candidates should possess a blend of required and desirable skills related to the aforementioned experiences, along with strong communication, organizational, and interpersonal abilities.
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Saskatoon, Saskatchewan, Canada University of Saskatchewan Full timePostdoctoral Researcher in Mechanical Engineering (Machine Learning Applications in Mining Safety)Primary Objective: The selected candidate will engage in the creation of a Machine Learning algorithm aimed at assessing the safety of underground mine structures through the analysis of audio data captured during tool impacts. This initiative is part of a...
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