Associate Lecturer - Machine Learning (HE1)

Conduct day time classes on supervised and unsupervised learning, covering main models for regression, classification, and clustering, while integrating Bayesian inference, Monte Carlo methods, and dimension reduction. Deliver theoretical foundations alongside real-world applications and datasets to support student understanding of high-dimensional data processing. Complete individual case study coursework and prepare for the two-hour unseen written examination. Requires a Ph.D. or Master's degree in a related discipline and at least two years of tertiary-level teaching experience. Applicants must be able to teach during standard daytime hours.