Postdoc: AI for Cancer Genomics & MM Therapy

Broad Institute; Cambridge (MA); On-site; USD 65,000 - 90,000; Full time

Benefits offered by this job

Health benefits

401(k) retirement plan

Paid holidays

Job summary

The Broad Institute's Getz Lab in Cambridge, MA seeks a highly motivated Postdoctoral Associate to lead AI-driven prediction models for multiple myeloma, integrating whole-genome sequencing, single-cell transcriptomics, immune profiling, and clinical data. Collaboration with Irene Ghobrial’s lab at Dana-Farber will bridge computational biology, machine learning, and clinical research.

You will contribute to discovering insights into disease progression and treatment response, while mentoring

Qualifications

• 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 (Python, R, Matlab preferred).
• Strong collaboration and communication with bioinformaticians, wet-lab researchers, and clinicians.
• Prior experience applying AI/ML methods in cancer is a plus.

Responsibilities

• Lead data analysis strategies to drive research projects, especially 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 diverse audiences including non-computational researchers.
• Prepare written reports and presentations for meetings.
• Opportunity to teach and mentor junior team members.

Skills

Machine learning

Programming

Communication skills

Collaborative

Data analysis

Education

PhD in a quantitative field

Tools

Python

R

Matlab

Job description

The Broad Institute's Getz Lab in Cambridge, MA seeks a highly motivated Postdoctoral Associate to lead AI-driven prediction models for multiple myeloma, integrating whole-genome sequencing, single-cell transcriptomics, immune profiling, and clinical data. Collaboration with Irene Ghobrial’s lab at Dana-Farber will bridge computational biology, machine learning, and clinical research.

You will contribute to discovering insights into disease progression and treatment response, while mentoring