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# Overview

- **Company:** UMass Med School
- **Location:** Worcester, MA, United States
- **Experience:** Starter
- **Salary:** $80,000.0
- **Contract:** Permanent contract
- **Skills:** Data Analysis, Big Data, Bioinformatics, Computational Biology, Data Integration, Data Visualization, Python (Programming Language), Machine Learning, Workflow Management Systems, Fitbit, Docker
- **Published:** September 28, 2026
- **Apply:** https://www.juju.com/job/21_01a044b7-d658-7ccb-b4b2-e0ef5cd9c27a

## About the Role

* 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., * 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.

## Description

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 (vigor.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.

 1. 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

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.


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