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Computational Biologist

Job in Boston, Suffolk County, Massachusetts, 02298, USA
Listing for: Dana-Farber Cancer Institute
Full Time position
Listed on 2026-03-08
Job specializations:
  • Research/Development
    Research Scientist
Salary/Wage Range or Industry Benchmark: 60000 - 80000 USD Yearly USD 60000.00 80000.00 YEAR
Job Description & How to Apply Below

Position Overview

Dana‑Farber Cancer Institute/HHMI/Harvard Medical School/Broad Institute – Computational Biology Position in the Kadoch Laboratory

Kadoch Laboratory

Chromatin and gene regulation in human disease

The Kadoch Laboratory at the Dana‑Farber Cancer Institute, Harvard Medical School, Broad Institute, and Howard Hughes Medical Institute is seeking a highly qualified individual who has recently obtained or is about to obtain a BA/BS degree to join our vibrant research team as a Computational Biologist focused on cancer biology, epigenetics, and chromatin regulation. Our lab uses multidisciplinary approaches including biochemistry, biophysics, structural biology, chemical biology, and functional genomics, epigenomics, and AI‑based approaches to explore the mechanisms of chromatin remodeling complexes, which are among the most frequently mutated cellular entities in human cancers and other diseases.

As such, our computationally centered projects in the lab are highly diverse and involve extensive genomics (i.e. analysis of diverse sequencing methods including DNA‑sequencing, RNA‑sequencing (RNA‑seq), ATAC‑seq, ChIP‑seq, CUT&RUN, CUT&TAG, single‑cell ATAC+RNA‑seq, among other approaches), analysis of functional screening datasets (i.e. genome‑wide as well as targeted CRISPR‑ and base editing‑based screens for cell fitness or other cellular outcomes), 3D structural biology (i.e. use of Pymol, UCSF Chimera for structural analysis, mapping mutations, etc.),

analysis and integration of mass‑spectrometry proteomics datasets, and artificial intelligence/machine learning (AI/ML) and systems‑biology‑focused efforts (i.e. large genomics and proteomics dataset analysis and integration, Deep Mind Alphafold, Rosetta, other approaches). Our exciting collection of ongoing projects involve collaborations with laboratories across the Harvard and MIT research centers, hospitals in Boston and Cambridge, as well as with groups across the country and internationally.

This is a unique opportunity with significant potential for the student to work directly with the PI as well as with senior postdoctoral fellows, graduate students and medical students. In addition to working as part of a team(s), the student will carry forward independent projects resulting in authorship on publications and opportunities to present work at local, national, and international meetings.

Responsibilities
  • Data Processing:
    Uses existing tools to build data processing pipelines to convert raw data into formats compatible with conventional statistical analysis and visualization
  • Data Analysis:
    Performs routine analysis for which established tools exist and are considered reliable. Keep up with the computational biology literature to assure pipelines components are up to date
  • Data and Software Management:
    Monitors, downloads, organise, and manages data from public data repositories or generated by collaborates. Evaluate published tools and updates pipeline as necessary.
  • Manuscript Preparation:
    Drafts the computational biology sections of a manuscript; assists in writing the results section; checks manuscripts for numerical accuracy; prepares tables and figures
  • Grant Preparation:
    Helps to formulate specific aims, explains options for experimental design, and develops data analysis plans
  • Research Portfolio Management:
    Develops timelines and components for multiple routine projects; masters multi‑tasking so that complex projects involving many interdisciplinary individuals move forward smoothly
  • Team interactions:
    Offers peer‑to‑peer training for new statisticians in design, analysis, and presentation of results. It is expected that insight into career growth will be offered to more junior statisticians
Knowledge, Skills, and Abilities Required
  • Computational

    Skills:

    Knowledge of UNIX/Linux. Familiarity with scripting in Python and statistical programming using R
  • Data analysis

    Skills:

    Familiarity with principles of experimental design and the modern data analysis paradigms is required
  • Collaboration:

    Able to discuss and present results, share ideas accurately and communicate them effectively, both in writing and verbally
  • Strong interpersonal skills – ability…
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