Postdoc, Computational Oncology & Multi-omics

Dana-Farber Cancer Institute (DFCI); Boston (MA); On-site; USD 72,000 - 76,000; Full time

Job summary

Dana-Farber Cancer Institute in Boston invites applications for a Postdoctoral Research Fellow in the Ghobrial Lab to conduct computational studies on multiple myeloma and its precursor conditions. This role focuses on identifying novel biomarkers and leveraging multi-omic data for prognostication and monitoring therapy response.

The successful candidate will work at the intersection of computational biology, genomics, proteomics, pathology, AI, and clinical data science, developing predictive

Qualifications

• PhD in Bioinformatics, computational biology, systems biology, statistics, CS or MD with related experience.
• Strong programming in Python, R, with data analysis, ML, and statistics experience.
• Experience with multi-omic data integration and predictive modeling of outcomes.

Responsibilities

• Conceive and test statistical, ML, and network-based approaches for high-dimensional biological data.
• Design predictive models integrating multi-omics and clinical data for drug response.
• Present findings at conferences and collaborate with internal teams and external partners.
• Maintain notebooks and documentation for reproducibility and continuity.
• Contribute to grants, manuscripts, and progress reports.
• Mentor junior team members and support their development.

Skills

Python

R

ML models

Multi-omics

Teamwork

Education

PhD in Bioinformatics / Computational Biology / Cancer biology

MD with 2-3 years related experience

Tools

Jupyter

Genomic data tools

R

Job description

Dana-Farber Cancer Institute in Boston invites applications for a Postdoctoral Research Fellow in the Ghobrial Lab to conduct computational studies on multiple myeloma and its precursor conditions. This role focuses on identifying novel biomarkers and leveraging multi-omic data for prognostication and monitoring therapy response.

The successful candidate will work at the intersection of computational biology, genomics, proteomics, pathology, AI, and clinical data science, developing predictive