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 PubMed). 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.

Environment/Lab Culture: Our lab is comprised of 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.

Our ideal postdoc candidate: We are seeking a highly motivated researcher to explore deep learning approaches to model metabolic changes within tumors in order to predict metabolic vulnerabilities in hypoxic tumors. This candidate will bring experience in leveraging deep learning foundation models in cancer research.

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 that help answer the most challenging questions in cancer.

The successful candidate will bring strong computational and statistical skills (e.g., a background in Computational Biology, Biology, Machine Learning, Statistics, Medicine, Physics, Chemistry, Engineering, Mathematics, Computer Science, or other related fields) to the lab as well as enthusiasm for learning on the job. In return, you will develop many core competencies to prepare you for the next stages of your career. Come and bring your energy, intellectual curiosity, and computational skills/talents to this world-class dynamic team!

Role Expectations
* Play a lead role in designing and executing data analysis strategies to support research projects
* Explore and develop tools for analyzing novel data types.
* Develop new deep learning methodologies for integrating data and predicting tumor outcome, subtypes, molecular mechanisms, and response to therapy.
* Conceive, implement and test statistical models; analyze data from experiments.
* Present results to a variety of audiences, including non-computational researchers.
* Prepare written reports (e.g., manuscripts, grants, patents) and presentations for meetings.
* Opportunity to teach and mentor junior team members.

Requirements
* A PhD in Bioinformatics, Chemistry, Computer Science, Engineering, Mathematics, Statistics, Physics, or a related quantitative discipline
* Fast learner, analytical thinker, creative, "hands-on", team-player.
* Experience with computational analysis, algorithm development and statistics.
* Proficiency in at least one modern programming language. Experience with a scientific programming environment (such as Python, R, or Matlab) is preferred.
* Strong communication skills.
* Background in machine learning or biology is a plus.
* Knowledge of cancer genomics is a plus but is NOT required. Inclination to acquire such knowledge is imperative.

Keywords: Cancer, Personalized Medicine, Genomics, Machine Learning, Computational biology, Statistics, Cancer resistance, Biomarker discovery, Computational modeling, Tumor evolution, Predictive models, Tumor Microenvironment.