Computational Biologist

UMass Med School; Worcester (MA); On-site; USD 80,000 - 90,000; Full time

Job summary

UMass Med School in Worcester, MA seeks a Computational Biologist to join an interdisciplinary team studying autoimmune diseases. You will analyse bulk and single-cell datasets, and integrate multi-omic and clinical data to model disease mechanisms.

The role combines computational and statistical work with a biology-driven mindset, requiring strong programming in R/Python and experience in modern omics analyses.

Qualifications

• Master's degree in quantitative field; Ph.D. preferred.
• 1–3 years of related experience.
• Proficiency in R or Python for analysis and visualization.
• Experience with linear/mixed models and ML approaches.

Responsibilities

• Process, analyze, and interpret large-scale datasets (bulk/single-cell RNA-seq, ATAC-seq, proteomics, spatial transcriptomics).
• Develop new analysis methods as needed during investigations.
• Perform clustering, trajectory inference, and regulatory network reconstruction.
• Collaborate with clinicians and immunologists to form testable hypotheses.
• Integrate genetic, molecular, and clinical features for mediation and causal inference.
• Model temporal dynamics using wearable and clinical data.
• Present results in meetings and conferences; contribute to manuscripts.
• Generate publication-quality figures and interactive visualizations.

Skills

Statistical modeling

Data visualization

Machine learning approaches

R

Python

Unix/Linux

HPC / Cloud computing

Education

Master's degree in Computational Biology or related field

Ph.D. strongly preferred

Tools

R

Python

Unix/Linux

HPC / Cloud

Job description

Computational Biologist

Requisition Number: 2026-49909

# 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:

1. 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.
2. 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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