Postdoctoral Associate, Computational Biomedicine

About the Role

The Beane Lab within the section of computational biomedicine at Boston University Chobanian & Avedisian School of Medicine is seeking a highly motivated computational postdoctoral associate. This role will lead the analysis of single-cell and spatial transcriptomic data across two translational research programs in lung and urethral disease. The successful candidate will join a collaborative group of computational biologists, bench scientists, pathologists, and clinicians, and will take intellectual ownership of projects from data processing through biological interpretation and publication.

Key Responsibilities

• Lead the analysis of single-cell and spatial transcriptomic data.
• Take intellectual ownership of projects from data processing through biological interpretation and publication.

Requirements & Tech Stack

• PhD (or equivalent) in computational biology, bioinformatics, genomics, biostatistics, systems biology, or a related quantitative field, obtained within the last [3] years or expected before the start date.
• Hands-on experience analyzing single-cell and/or spatial transcriptomic data, with a first-author publication or preprint demonstrating this work.
• Proficiency in R and/or Python.
• Experience with standard analysis frameworks (e.g., Seurat, Bioconductor, scanpy, squidpy, scvi-tools).
• Comfort working in a Linux/high-performance computing environment.
• Working knowledge of, or a strong willingness to learn, the underlying biology, including epithelial and immune cell biology, tissue histology, and the pathophysiology of lung and urethral disease.
• Excellent written and verbal communication skills in English.
• Ability to work both independently and as part of a multidisciplinary team.

Qualifications (Preferred)

• Background in cancer biology, immunology, pulmonary or urologic disease, or tissue pathology.
• Experience with multimodal data integration.
• Experience with spatial neighborhood analysis.
• Experience with machine learning applied to genomic or imaging data.
• Experience with workflow management (e.g., Nextflow, Snakemake).
• Experience with containers.
• Experience with version control (Git).

Application Instructions

Please submit the following to Dr. Jennifer Beane ([email protected]) with "Postdoctoral Associate – Single-Cell and Spatial Transcriptomics" in the subject line:

• A cover letter (one page) describing your research experience, your interest in the position, and how your background prepares you to work at the interface of computation and lung or urethral biology.
• A current CV including publications and links to code (e.g., GitHub).
• Names and contact information for three references.