Computational Biologist

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    "description": "<h2>Overview</h2>\n<p>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.</p>\n<p> </p>\n<p>This position will bridge two complementary areas of research:</p>\n<li><strong>Molecular systems immunology</strong>, 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.</li>\n<li><strong>Genetic and longitudinal modeling</strong>, 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.</li>\n<p> </p>\n<p>The ideal candidate combines strong computational and statistical skills with a biological curiosity about how genetic and environmental factors jointly shape immune dysregulation.</p>\n<h2>Responsibilities</h2>\n<p>Responsibilities</p>\n<p> </p>\n<ul>\n <li>Process, analyze, and interpret large-scale datasets including bulk and single-cell RNA-seq, ATAC-seq, proteomics, and spatial transcriptomics.</li>\n <li>Develop new analysis methods as needed and as they arise during investigations</li>\n <li>Perform clustering, trajectory inference, and regulatory network reconstruction to define immune cell states and pathways relevant to autoimmune pathogenesis.</li>\n <li>Work closely with clinicians, immunologists, and experimentalists to formulate biologically grounded hypotheses and computational analyses.</li>\n <li>Integrate genetic, molecular, and clinical features to identify mediators linking genotype to phenotype using mediation and causal inference frameworks (e.g., Bayesian networks).</li>\n <li>Combine data from wearable sensors (e.g., Fitbit activity, sleep, heart rate), clinical surveys, and biomarker measurements to model temporal dynamics of disease activity.</li>\n <li>Present findings in lab meetings, consortium calls, and scientific conferences; contribute to manuscripts and grant proposals.</li>\n <li>Generate publication-quality figures and interactive visualizations that communicate complex data intuitively.</li>\n</ul>\n<h2>Qualifications</h2>\n<p>Required Qualifications</p>\n<ul>\n <li>Master’s degree in Computational Biology, Bioinformatics, Genetics, Statistics, Physics, Math or a related quantitative field; Ph.D. strongly preferred.</li>\n <li>1-3 years of related experience</li>\n <li>Strong proficiency in R or Python, statistical modeling, and data visualization.</li>\n <li>Strong understanding of linear models, mixed-effect models, and in general machine learning approaches to complex datasets.</li>\n <li>Experience working in Unix/Linux environments and using HPC or cloud-based computational resources.</li>\n</ul>\n<p> </p>\n<p> </p>\n<p> </p>\n<p>Preferred Qualifications</p>\n<ul>\n <li>Background in human genetics or clinical genomics, including genotype imputation, association testing, and fine-mapping.</li>\n <li>Experience with integrative or multi-omic data analysis and familiarity with single-cell and spatial transcriptomic data.</li>\n <li>Knowledge of causal inference, longitudinal modeling, or Bayesian hierarchical modeling.</li>\n <li>Exposure to wearable-device or digital-phenotyping datasets and experience linking such data to molecular or clinical outcomes.</li>\n <li>Understanding of immunology or autoimmune disease biology.</li>\n <li>Familiarity with containerization (Docker/Singularity), workflow management systems (Snakemake, Nextflow), and reproducible-research practices.</li>\n</ul>\n<h2>Additional Information</h2>\n<p>#LI-KR1</p>",
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