Together, the UBC DUS, VPC and MIUS offer a uniquely collaborative ecosystem integrating access to well characterized clinical cohorts, state-of-the-art genomic technologies, robust computational infrastructure, and deep interdisciplinary expertise, creating a vibrant academic community committed to improving patient outcomes through innovation and evidence-based practice. Reporting to the Head of the UBC Department of Urologic Sciences, the successful candidate will be expected to develop and lead an independent research program in computational oncology, centred on new quantitative and machine-learning methods, including artificial intelligence and generative modelling, that integrate single-cell genomics and spatial profiling to study cancer evolution, therapy resistance, and immune escape in urologic cancers (especially prostate, bladder and kidney cancer). The program will draw on expertise in probabilistic modeling and causal machine learning, phylogenetic and evolutionary inference, clonal dynamics, and immune-tumour interactions, and will emphasize rigorous uncertainty quantification and reproducible, scalable analysis of large multi-omics datasets. The successful candidate will be expected to work closely with clinical and experimental collaborators to translate computational findings into testable hypotheses, biomarkers, therapeutic targets, and decision-relevant insights. The successful candidate will be expected to participate in the teaching activities of the Department, as well as provide mentorship and training to undergraduate, graduate, and postgraduate learners.