Postdoctoral Associate - Getz Lab

The Getz Lab (at the Broad Institute and MGH ) is a world-leading laboratory for cancer genome analysis. We develop highly innovative, robust, and widely-used computational methods to study the molecular basis of cancer, including genomic alterations that drive primary and resistant tumors, cell-of-origin, premalignant lesions, mutational processes, activity of different pathways, and microenvironmental changes. We then follow up key findings experimentally.

While the comprehensive analysis of cancer genomes is ongoing, major barriers still exist in converting this information to patient benefit and achieving the goal of personalized medicine. Our work stands at the forefront of cancer genome science, and our research is regularly published in top-tier journals (see our work on Google Scholar and Pub Med ). We are dedicated to innovating and pushing the limits of what we know and what can be known in understanding the complexities of human cancer.

We are an interdisciplinary group of scientists, engineers, and clinicians who work together in a mutually supportive and respectful environment. Ideas are freely shared, and contributions are highly valued. Moreover, Dr. Getz places a high priority on mentoring postdoctoral trainees to work toward achieving their career paths and goals, and his lab, as well as the environments at the Broad Institute and Massachusetts General Hospital, provide frequent and varied educational and skill-building opportunities.

The lab is engaged in the larger Boston-area ecosystem and the cancer research community worldwide, and provides a vibrant research environment for your contributions to be disseminated and recognized in the field. Our ability to integrate both computational and wet-lab work enables us to address key questions at a deeper and more impactful level. Indeed, we constantly use and develop new technologies to help unlock new findings.

We are seeking a highly motivated computational postdoc to take primary leadership for developing an AI-driven prediction model for multiple myeloma (MM) that integrates multi-modal information (e.g., from whole-genome sequencing, single-cell transcriptomics, immune profiling, and clinical data) to identify patients who are likely to respond, or become resistant, to therapy.

This work will be conducted over the next 2 years (with a potential for follow-on work beyond the next 2 years) in close collaboration with Dr. Irene Ghobrial's laboratory, a world-leading group in multiple myeloma research at the Dana-Farber Cancer Institute, and will straddle the intersection among computational biology, machine learning, and clinical research. The successful candidate will bring their demonstrated expertise in artificial intelligence, machine learning, and statistical modeling to reveal insights that will better understand disease progression, therapeutic response, and mechanisms of resistance in MM, and will help to reach our collective goal of delivering precision medicine to MM by matching patients to their most likely-to-succeed therapeutic strategy.

As a member of our team, you will collaborate with other scientists, engineers, and clinicians in a collegial work environment with an emphasis on intellectual rigor. Indeed, our collective brainpower and creativity––our best asset––creates an excellent environment for deep innovation, out-of-the-box thinking, and creative problem solving. We will teach you what you do not yet know through mentoring, peer support, and many educational opportunities (e.g., floor talks, regular meetings, boot camps, journal clubs, conferences, etc.),

and we will work together to make discoveries…