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# Postdoctoral Associate - The Getz Lab, Multiple Myeloma - Machine Learning

- **Company:** Broad Institute
- **Location:** Cambridge, MA, United States
- **Experience:** Starter
- **Salary:** $70,000 - $92,667
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Algorithm Design, Data Analysis, Bioinformatics, Clinical Data Repository, Computational Biology, Python (Programming Language), MATLAB, Machine Learning, Information Technology, Programming Languages
- **Published:** October 15, 2026
- **Apply:** https://broadinstitute.avature.net/en_US/careers/ApplicationMethods?jobId=22541

## About the Role

* A PhD in Bioinformatics, Computer Science, Machine Learning, Engineering, Mathematics, Statistics, Physics, or a related quantitative discipline.
 * 0-1+ years of post-graduate experience.
 * Demonstrated experience developing and validating machine learning models.
 * 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.
 * Highly collaborative, with ability able to work on projects alongside fellow bioinformaticians, wet-lab experimentalists, and clinicians.
 * Demonstrated experience with conducting rigorous and reproducible research in a fast-paced environment.
 * Fast learner, analytical thinker, creative, "hands-on," team-player.
 * Strong oral and written communication skills.
 * Prior experience applying AI/ML methods in cancer is a plus.
 * Knowledge of cancer genomics is a plus but is NOT required. Inclination to acquire such knowledge is imperative.

## Description

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 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 drive research projects, especially pertaining to building AI-driven prediction models in MM.
 * Explore and help design tools for analyzing novel data types.
 * 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.