Faculty Opportunity in Cancer Research - Machine Learning

The Cancer Plasticity and Dormancy Program is a research program established to prevent cancer relapse. Our vision is to discover the fundamental cellular and molecular mechanisms that control cancer cell dormancy, cancer plasticity and cancer relapse. The goal of the program is to generate tools to predict which patients are at risk of relapse, to identify and develop new targets to stop cancer relapse before it occurs, and to accelerate the clinical translation of relapse-preventing therapies. The program has established the international AllClear initiative, supported by the inaugural National Breast Cancer Foundation collaborative research accelerator grant.

The Garvan Institute invites applicants for a Faculty-level position to join the Cancer Plasticity and Dormancy Program. We are seeking individuals to build upon existing research and international partnerships in data science, computational biology and machine learning, including representation learning and geometric deep learning approaches.

We will consider applications from individuals with a strong track record in computational biology and expertise in machine learning tool development. Alongside their independent research program, they will develop approaches that can be applied to solve fundamental biological questions in cancer and immunology.

They will have demonstrated an aptitude for independent, creative thinking and a strong track record of high-quality scientific output, relative to opportunity and career stage. They will contribute to program-specific cancer questions and to multidisciplinary collaborations across Garvan and other national and international institutions.

A generous start-up package will be provided to encourage the pursuit of high risk/high reward research with transformative potential. We welcome local and international applicants who show strong alignment with the Garvan Institute's vision for impactful discovery research.

We especially welcome applicants from diverse backgrounds and those seeking the academic freedom to pursue discovery research wherever it takes them.