Computational Biologist (2026-49909)

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When you join the UMass Chan Medical School team, you join us in advancing together to improve the health and well-being of our diverse communities throughout Massachusetts and across the US and the world. Together, we lead in education, research, health care delivery, and public service. Your lifes work is more than a career. Its an expression of your passion, intellect, skill, and drive. UMass Chan"s commitment to excellence, innovation, competitive benefits, and work-life integration will allow you to build a professionally rewarding career as we work together to better or improve the health of people around the globe.

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Computational Biologist

Job Number: 2026-49909

Category: Research

Location: Worcester, MA

Shift: Day

Exempt/Non-Exempt: Exempt

Business Unit: UMass Chan Medical School

Department: School - Genomics and Computational Biology - W403700

Job Type: Full-Time

Num. Openings: 1

Post Date: May 14, 2026

Work Location: 100% Onsite

Salary Minimum: USD $80,000.00/Yr.

Salary Maximum: USD $90,000.00/Yr.

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 dataincluding proteomics, wearable device metrics, survey responses, and clinical measuresto 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

• Masters 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.

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Posting Disclaimer:

This job posting outlines the primary responsibilities and qualifications for the role but is not intended to be an exhaustive list. Duties and expectations may evolve in response to the needs of the department and the broader institution.

In alignment with our commitment to pay transparency, the base salary range for this position is listed above (exclusive of benefits and retirement). At UMass Chan Medical School, final base salary offers are determined based on a combination of factors, including your skills, education, and relevant experience. We also consider internal equity to ensure fair and consistent compensation across our teams.

Please note that the range provided reflects the full base salary range for this position. Offers are typically made within the midrange to allow for future growth and development within the role.

In addition to base pay, UMass Chan offers a comprehensive Total Rewards package, which includes paid time off, medical, dental, and vision coverage, and participation in a 401(a)-retirement plan, with the option to contribute to a voluntary 403(b) plan.

UMass Chan welcomes all qualified applicants and complies with all state and federal anti-discrimination laws.

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