Postdoctoral Research Fellow - Multiomics

Dana-Farber Cancer Institute (DFCI); Boston (MA); On-site; USD 72,000 - 76,385; Full time

Job summary

Dana-Farber Cancer Institute's Center for Functional Cancer Epigenetics seeks a Postdoctoral Research Fellow to advance multiomic analyses of patient responses to cancer therapies. This role blends computational biology, genomics, and AI, using clinical trial samples to build predictive models of treatment outcomes.

You will develop pipelines for integrating RNA-seq, ATAC-seq, ChIP-seq, spatial transcriptomics and single-cell data, collaborating with experimental and clinical teams to translate

Qualifications

• PhD in Bioinformatics, Computational Biology, Statistics, CS, or related field.
• Strong programming in Python/R with data analysis and ML experience.
• Experience with multiomic data (RNA-seq, ATAC-seq, ChIP-seq, spatial transcriptomics, single-cell).
• Familiarity with data integration, predictive modelling, and network analysis.
• Experience with cloud computing or HPC is a plus.
• Excellent communication and collaboration skills.

Responsibilities

• Develop computational pipelines for multiomic data integration across modalities.
• Apply statistical, ML, and network methods to high‑dimensional data.
• Collaborate with experimental and clinical teams to interpret results.
• Design and optimise predictive models of drug response, including digital twins.
• Assist in data visualization, manuscript writing, and grant applications.
• Maintain rigorous documentation and reproducibility of workflows.
• Contribute to dissemination of research findings.

Skills

Python

R

Data analysis

Machine learning

Statistics

Team collaboration

Education

PhD in Bioinformatics / Computational Biology

Tools

Snakemake

Cloud computing

HPC

Job description

Postdoctoral Research Fellow - Multiomics

Dana-Farber Cancer Institute

Boston, Massachusetts

Overview

We are seeking a highly motivated Postdoctoral Research Fellow to join the Center for Functional Cancer Epigenetics (CFCE) at Dana-Farber Cancer Institute and become part of a multidisciplinary project led by Paloma Cejas and Henry Long, in collaboration with AITIA, an artificial intelligence and computational biology company. The project focuses on understanding patient responses to drug treatments through multiomic analyses, including chromatin profiling, single-cell sequencing, and spatial transcriptomics, using samples derived directly from clinical trials. The successful candidate will work at the intersection of computational biology, genomics, epigenomics, artificial intelligence, and clinical data science, contributing to projects that leverage cutting‑edge multiomic datasets to develop predictive models of therapy response and identify clinically actionable biomarkers.

Key Responsibilities

• Develop and implement computational pipelines for the integration of multiomic datasets, including transcriptomics, chromatin profiling, genomics, spatial transcriptomics and single‑cell data.
• Apply statistical, machine learning, and network‑based approaches to analyze high‑dimensional biological data.
• Collaborate closely with experimental and clinical teams to interpret results and translate findings into actionable insights.
• Design and optimise predictive models of drug response, including the use of digital twin simulations.
• Assist in data visualization, interpretation, and presentation of results for publications, grant applications, and internal/external meetings.
• Maintain rigorous documentation, reproducibility, and quality control of computational workflows.
• Contribute to manuscript writing and dissemination of research findings.

Qualifications

• PhD in Bioinformatics, Computational Biology, Systems Biology, Statistics, Computer Science, or related field.
• Strong programming skills in Python, R, or equivalent, with experience in data analysis, statistics, and machine learning.
• Experience with multiomic datasets, including RNA‑seq, ATAC‑seq, ChIP‑seq, spatial transcriptomics and single‑cell data.
• Familiarity with data integration frameworks, predictive modelling, and network analysis.
• Experience with cloud computing, workflow management (Snakemake), or HPC environments is a plus.
• Strong analytical and problem‑solving skills, with attention to detail.
• Excellent communication and teamwork skills, with ability to collaborate across experimental and computational groups.

Preferred to have

• Experience in translational research or working with patient‑derived datasets.
• Familiarity with chromatin biology or epigenetics.
• Background in biomarker discovery or predictive modelling in oncology.

Dana‑Farber Cancer Institute is an equal opportunity employer and affirms the right of every qualified applicant to receive consideration for employment without regard to race, color, religion, sex, gender identity or expression, national origin, sexual orientation, genetic information, disability, age, ancestry, military service, protected veteran status, or other characteristics protected by law.

Pay Transparency Statement

The hiring range is based on market pay structures, with individual salaries determined by factors such as business needs, market conditions, internal equity, and based on the candidate’s relevant experience, skills and qualifications.

For union positions, the pay range is determined by the Collective Bargaining Agreement (CBA).

$72,000.00 - $76,385.00