Top 1 Marine biologist Jobs in Worcester, Ma

Computational Biologist
University of Massachusetts Medical School —Worcester, MA
- $80,000 - $90,000 a year
- Full-time
- Mid-level
- Master's degree
- Doctor of Philosophy

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Computational Biologist

University of Massachusetts Medical School -
Worcester, MA

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Job Details

Full-time
$80,000 - $90,000 a year

Qualifications

AI models
Data visualization software proficiency
Biostatistics-based research
UNIX
Master's degree
Machine intelligence
Statistics research
Data visualization projects
Biostatistical modeling

Full 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:

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.

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:

#LI-KR1

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