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 (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.

• 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.