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Computational Biologist

Job in Worcester, Worcester County, Massachusetts, 01609, USA
Listing for: UMass Med School
Full Time, Seasonal/Temporary position
Listed on 2026-08-13
Job specializations:
  • Research/Development
    Data Scientist, Research Scientist
Salary/Wage Range or Industry Benchmark: 80000 - 90000 USD Yearly USD 80000.00 90000.00 YEAR
Job Description & How to Apply Below

Computational Biologist

Requisition Number:

# of Openings: 1

Posted Date:
Day

Shift: Exempt

Position Type:
Full-Time

Job Location:

US-MA-Worcester

Minimum Salary: USD $80,000.00/Yr.

Maximum Salary: USD $90,000.00/Yr.

Overview

The Computational Biologist be part of an interdisciplinary research group combining systems biology, immunology, and human genetics to uncover the mechanisms that drive autoimmune disease. The lab leads large-scale efforts such as the VIGOR family-based vitiligo cohort (bigor.umassmed.edu) and multi-omic studies of lupus and cutaneous autoimmunity, integrating data across molecular, cellular, and clinical scales.

This position will bridge two complementary areas of research:

  • Molecular systems immunology, involving the analysis of single-cell and spatial transcriptomic, epigenomic, and proteomic datasets to dissect cell states and communication networks in diseased and healthy tissues.
  • Genetic and longitudinal modeling, integrating genomic variation with real-world longitudinal data-including proteomics, wearable device metrics, survey responses, and clinical measures-to build predictive and causal models of disease initiation and progression.
  • The ideal candidate combines strong computational and statistical skills with a biological curiosity about how genetic and environmental factors jointly shape immune dysregulation.

    Responsibilities
    • Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics.
    • Develop new analysis methods as needed and as they arise during investigations
    • Perform clustering, trajectory inference, and regulatory network reconstruction to define immune cell states and pathways relevant to autoimmune pathogenesis.
    • Work closely with clinicians, immunologists, and experimentalists to formulate biologically grounded hypotheses and computational analyses.
    • Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks).
    • Combine data from wearable sensors (e.g., Fitbit activity, sleep, heart rate), clinical surveys, and biomarker measurements to model temporal dynamics of disease activity.
    • Present findings in lab meetings, consortium calls, and scientific conferences; contribute to manuscripts and grant proposals.
    • Generate publication-quality figures and interactive visualizations that communicate complex data intuitively.
    Required Qualifications
    • Master's degree in Computational Biology, Bioinformatics, Genetics, Statistics, Physics, Math or a related quantitative field;
      Ph.D. strongly preferred.
    • 1-3 years of related experience
    • Strong proficiency in R or Python, statistical modeling, and data visualization.
    • Strong understanding of linear models, mixed-effect models, and in general machine learning approaches to complex datasets.
    • Experience working in Unix/Linux environments and using HPC or cloud-based computational resources.
    Preferred Qualifications
    • Background in human genetics or clinical genomics, including genotype imputation, association testing, and fine-mapping.
    • Experience with integrative or multi-omic data analysis and familiarity with single-cell and spatial transcriptomic data.
    • Knowledge of causal inference, longitudinal modeling, or Bayesian hierarchical modeling.
    • Exposure to wearable-device or digital-phenotyping datasets and experience linking such data to molecular or clinical outcomes.
    • Understanding of immunology or autoimmune disease biology.
    • Familiarity with containerization (Docker/Singularity), workflow management systems (Snakemake, Nextflow), and reproducible-research practices.
    Additional Information

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