Computational Biologist

Position Summary

The McIntire Lab is seeking a highly motivated Computational Biologist to support ongoing and expanding research programs in neurodegeneration, multi-omics biomarker discovery, and spatial data integration. The laboratory integrates PET and MRI imaging, immunoassays, lipidomics, transcriptomics, and spatial omics technologies to investigate mechanisms of Alzheimer’s disease and related disorders. The selected candidate will provide computational support across multiple large-scale projects and will contribute to data processing, analysis pipeline development, and scientific reporting.

Job Responsibilities

• Process, integrate, and analyze multimodal datasets including PET/MRI biomarkers, plasma proteomics, lipidomics, single-cell and spatial transcriptomics (Visium, CosMx), and DESI lipid imaging.
• Implement and maintain reproducible computational pipelines in R and Python following the laboratory’s established workflows.
• Support data harmonization and cross-cohort analyses for large consortia datasets (ADNI, ROSMAP, BHII internal cohorts).
• Contribute to the development of new analysis frameworks for spatial multi-omics integration.
• Generate figures, tables, QC reports, and analysis summaries for manuscripts, grants, and presentations.
• Collaborate closely with wet-lab and imaging teams to ensure proper data structure, metadata tracking, and pipeline consistency.
• Participate in lab meetings, present analysis updates, and document computational procedures for internal use.

Education

• Bachelor's Degree in biology, computer science, bioinformatics, biomedical engineering, data science, or a related field.

Experience

Experience with high-dimensional data, including single-cell or spatial transcriptomics.

Experience with Neuro Degeneration biomarker datasets (PET, MRI, immunoassays, ADNI).

Prior experience working in collaborative, fast-paced research environments.

Familiarity with cloud computing, reproducible scripting, and version control (Git/GitHub).

Familiarity with lipidomics workflows (LipidSigR, maplet, mass spectrometry preprocessing).

Knowledge, Skills and Abilities

• Strong proficiency in R and Python, with experience in multi-omics analysis workflows (Seurat, Scanpy/Squidpy, Giotto, WGCNA and lipidomics pipelines).
• Ability to interact with multidisciplinary teams and translate biological questions into computational tasks.

Licenses and Certifications

Working Conditions/Physical Demands

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