Computational Biologist

UMass Chan Medical School; Worcester (MA); On-site; USD 75,000 - 95,000; Full time

Job summary

UMass Chan Medical School is seeking a Computational Biologist to join their interdisciplinary research team focused on autoimmune diseases. The role involves processing and analyzing large-scale datasets such as RNA-seq and proteomics, as well as developing new analytical methods. Ideal candidates will have a Master's degree and strong proficiency in R or Python. The position offers an opportunity to work closely with experts in immunology and human genetics, contributing to significant research projects.

Qualifications

• 1-3 years of related experience.
• Strong understanding of complex datasets.

Responsibilities

• Process and analyze large-scale datasets including RNA-seq, ATAC-seq, proteomics.
• Develop new analysis methods as necessary.
• Perform clustering and trajectory inference to define immune cell states.
• Integrate genetic, molecular, and clinical features for analysis.
• Present findings and contribute to publications.

Skills

Strong proficiency in R or Python

Statistical modeling

Data visualization

Linear models

Mixed-effect models

Machine learning

Unix/Linux environments

HPC or cloud-based resources

Education

Master’s degree in Computational Biology or related field

Ph.D. in a related quantitative field

Tools

R

Python

Docker

Singularity

Snakemake

Nextflow

Job description

Overview

The Computational Biologist will 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.

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 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.

The University of Massachusetts Chan Medical School welcomes all qualified applicants and complies with all state and federal anti‑discrimination laws.