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Computational Immunology Jobs (NOW HIRING)

The University of Chicago

Staff Scientist

Chicago, IL · On-site +1

The role emphasizes exploratory work at the intersection of immunology and AI. The Staff Scientist will work in a collaborative environment with computational and experimental investigators in the ...

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How much do computational immunology jobs pay per hour?

As of Aug 17, 2026, the average hourly pay for computational immunology in the United States is $54.93, according to ZipRecruiter salary data. Most workers in this role earn between $46.88 and $73.56 per hour, depending on experience, location, and employer.

What is computational immunology?

A Computational Immunology job involves using data science, bioinformatics, and mathematical modeling to study the immune system. Professionals in this field analyze large biological datasets, develop algorithms, and create simulations to understand immune responses and disease mechanisms. They work in academia, biotechnology, and pharmaceutical industries, contributing to vaccine development, immunotherapy, and precision medicine. The role requires expertise in immunology, programming, and statistical analysis.

What does a computational immunology professional do?

A professional in Computational Immunology typically spends their day designing and executing computational analyses of immunological data, such as sequencing or single-cell datasets. They often collaborate with experimental immunologists, bioinformaticians, and clinicians to interpret results and guide research directions. Tasks might include developing predictive models, visualizing complex immune system interactions, and reporting findings in team meetings or publications. Due to the interdisciplinary nature of the work, strong communication and adaptability are essential, as projects often evolve quickly with new scientific discoveries.

What are the key skills and qualifications needed to thrive in computational immunology?

To thrive in Computational Immunology, you need a strong background in immunology, bioinformatics, and computer science, typically supported by an advanced degree (MS/PhD) in these fields. Proficiency with programming languages such as Python or R, experience with high-throughput data analysis, and familiarity with platforms like Bioconductor or Cytoscape are highly valued. Excellent problem-solving, teamwork, and communication skills help professionals interpret complex data and present findings to multidisciplinary teams. These combined technical and soft skills are essential for advancing research in immunological mechanisms and driving innovation in areas such as vaccine development or immunotherapy.

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computational immunology jobs near you
Infographic showing various Computational Immunology job openings in the United States as of August 2026, with employment types broken down into 1% Internship, 53% Full Time, 45% Part Time, and 1% Contract. Highlights an 49% Physical, 2% Hybrid, and 49% Remote job distribution, with an average salary of $114,249 per year, or $54.9 per hour.

Computational Biologist

University of Massachusetts Medical School

Worcester, MA • On-site

$80K - $90K/yr

Full-time

Re-posted 4 days ago

Job description

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
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.

Qualifications
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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