Postdoctoral Associate - The Getz Lab
About The Position
The Getz Lab, a world-leading laboratory for cancer genome analysis at the Broad Institute and MGH, develops innovative computational methods to study the molecular basis of cancer. Their research focuses on genomic alterations driving tumors, cell-of-origin, premalignant lesions, mutational processes, pathway activity, and microenvironmental changes, with experimental follow-up. The lab aims to translate comprehensive cancer genome analysis into patient benefit and personalized medicine. Their work is published in top-tier journals, and they are dedicated to advancing cancer research. The lab fosters an interdisciplinary, supportive, and respectful environment where ideas are freely shared and contributions are valued. Dr. Getz prioritizes mentoring postdoctoral trainees, offering numerous educational and skill-building opportunities through the Broad Institute and Massachusetts General Hospital. The lab is connected to the broader Boston-area and global cancer research communities, providing a vibrant research environment for dissemination and recognition. Their integration of computational and wet-lab work allows for deeper and more impactful research, utilizing and developing new technologies. This specific role seeks a highly motivated researcher to lead computational analysis for projects investigating the mechanisms of multiple myeloma initiation, progression, and relapse. The postdoc will primarily analyze spatial multi-omics data (spatial transcriptomics, single-cell spatial proteomics, digital pathology) in ongoing multiple myeloma patient cohort studies, collaborating closely with Dr. Irene Ghobrial’s lab at DFCI. The successful candidate will join a collegial team emphasizing intellectual rigor, innovation, and creative problem-solving. Mentoring, peer support, and educational opportunities (floor talks, meetings, boot camps, journal clubs, conferences) will be provided to help the candidate develop core competencies for their career progression. The role requires strong computational and statistical skills, with an enthusiasm for on-the-job learning, and an invitation to bring energy, intellectual curiosity, and computational skills to a dynamic, world-class team.
Requirements
- A PhD in Bioinformatics, Computer Science, Machine Learning, Engineering, Mathematics, Statistics, Physics, or a related quantitative discipline.
- 0-1+ years of post-graduate experience.
- Experience with computational analysis, algorithm development, and statistics.
- Sufficient hands on experience in methods to analyze spatial multi-omic data is required; experience in analyzing data from other emerging technologies is a plus.
- Familiarity with a wide variety of bioinformatic analyses, including analyses of data from single-cell RNA sequencing (scRNA-seq), immune BCR/TCR sequencing, and bulk- and single-cell whole-genome sequencing (WGS)
- Proficiency in at least one modern programming language. Experience with a scientific programming environment (such as Python, R, or Matlab) is preferred.
- Demonstrated experience with conducting rigorous and reproducible research in a fast-paced environment
- Highly collaborative, with ability able to work on projects alongside fellow bioinformaticians, wet-lab experimentalists, and clinicians.
- Strong oral and written communication skills.
- Fast learner, analytical thinker, creative, hands-on, team-player
Nice To Haves
- 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.
Responsibilities
- Play a lead role in designing and executing data analysis strategies to support research projects, especially pertaining to spatial multiomics-omics data.
- Serve as a lead in the project, driving the project's scientific vision, identifying key biological questions and spearheading the computational strategies necessary to address them.
- Explore and develop tools for analyzing novel data types.
- Develop new spatial and genomic analysis 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.
Benefits
- medical, dental, vision, life, and disability insurance
- a 401(k) retirement plan
- flexible spending and health savings accounts
- at least 13 paid holidays
- winter closure
- paid time off
- parental and family care leave
- an employee assistance program
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What This Job Offers
Job Type
Full-time
Career Level
Entry Level
Education Level
Ph.D. or professional degree
