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making and machine learning, with real-world testing and feedback. The successful applicant will work on decision making for anomaly detection, behaviour analysis and surveillance decisions, under
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contribute to advancing simulation-based testing methods for ADS. You will contribute to cutting-edge research projects, including the EPSRC-funded SimpliFaiS: Simplification of Failure Scenarios for Machine
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science. Preferred Qualifications Enrollment: Must be a current graduate student (Master’s or PhD) in a relevant field (e.g. Computer Science, Data Science, Learning Sciences, Educational Technology, or
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sustaining a diverse workforce is key to the successful pursuit of excellence in research, innovation, and learning for all faculty, staff and students. Our commitment to employment equity helps achieve