Assistant Professor, Computational Oncology and Single Cell Genomics

University of British Columbia•Vancouver, BC

4d•CA$120,000•Onsite

About The Position

The Department of Urologic Sciences at The University of British Columbia (UBC) invites applications for a full‑time, research focused faculty position in computational oncology at the rank of Assistant Professor, Grant Tenure Track. The Department of Urologic Sciences (DUS), part of the UBC Faculty of Medicine, strives to develop and deliver programs of excellence in key areas of urology. With a robust and active clinical and research faculty, the Department is a leader in education, patient care and research. The successful candidate will also be appointed as Senior Research Scientist in the Vancouver Prostate Centre and Centre for Urologic Oncology of the M.H. Mohseni Institute of Urologic Sciences providing access to cutting-edge facilities and collaborative research networks, opportunities for leadership in translational science, and a vibrant academic community committed to improving patient outcomes through innovation and evidence-based practice.

Requirements

PhD in Computational Biology, Bioinformatics, or a closely related field, with a demonstrated focus on quantitative modeling of cancer evolution and clonal dynamics using single-cell whole genome sequencing and/or longitudinal patient or experimental data.
Research expertise in causal machine learning, computational modeling, and single-cell multi-omics.
Experience modeling mechanism of genome structural dynamics in clinical cohorts of patients with cancer.
Proven ability to develop novel computational methodologies, including Bayesian generative models, phylogenetic inference tools, and algorithms for multimodal data integration.
Demonstrated history of collaboration with clinicians and experimentalists, including work on patient-linked cohorts or translationally relevant experimental systems.
Prior experience teaching computational statistics, Bayesian analysis, and software engineering.
Demonstrated evidence of ability in teaching and scholarly activity.
Willingness to respect diverse perspectives, including perspectives in conflict with one’s own, and a commitment to enhancing one’s own awareness, knowledge, and skills related to equity, diversity, and inclusion.

Responsibilities

Develop and lead an independent research program in computational oncology, centred on new quantitative and machine-learning methods 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).
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.
Work closely with clinical and experimental collaborators to translate computational findings into testable hypotheses, biomarkers, and decision-relevant insights.
Participate in the teaching activities of the Department, as well as provide mentorship and training to undergraduate, graduate, and postgraduate learners.
Provide service to the University and broader academic community, including peer review, committee participation, and initiative development.
Work collaboratively across disciplines to advance strategic initiatives for the Department and Faculty.
Contribute to fostering an environment that promotes inclusivity and embodies values of respect, integrity, compassion, collaboration, and equity.