Job Description

Baranov lab at the Harvard Medical School / Massachusetts Eye and Ear is looking for a Research Fellow in Single-cell and Spatial Transcriptomics to support the development of cell therapies for neurodegenerative diseases, including Glaucoma, Neurofibromatosis, Alzheimer’s, and Leber’s Hereditary Optic Neuropathy.

We are a diverse team, particularly interested in the functional integration of stem cell-derived organoids and neurons and the role that the retinal, brain, and optic nerve microenvironment plays in this process. Our multidisciplinary group combines experience in cell therapy, transplantation, stem cell differentiation, retinal development, retinal cell biology, retinal electrophysiology, small and large animal models of disease, bioinformatics, and single-cell and spatial advanced transcriptomics analysis. The lab is supported by grants from the BrightFocus Foundation, Gilbert Family Foundation, Massachusetts Lions, and NIH/NEI Audacious Goal Initiative. Being at the forefront gives us unique access to the experts in the field and multi-omics datasets.

The Harvard Medical School, Schepens Eye Research Institute and Massachusetts Eye and Ear provide an outstanding environment and visa support for research and career development. We have state-of-the-art facilities for benchwork and functional animal studies, access to on-site workstation / cloud computing for heavy computations and image processing. The mentoring program is supported by NIH T32 Training Grant. We are affiliated with the Harvard Stem Cell Institute which brings together talented scientists from different fields, and fosters networking and collaborations. My lab alumni work in industry, clinical settings, and academia.

We are seeking a highly motivated computational scientist with a strong background in single-cell or/and spatial transcriptomics to join our interdisciplinary research team. The successful candidate will lead computational analysis pipelines for single-cell and spatial omics datasets and atlases and contribute to biological discovery projects focused on neural and glial biology, development, and disease.

Key Responsibilities

· Design, iteratively update, implement, and maintain analysis pipelines for single-cell RNA-seq, single-nucleus RNA-seq, and spatial transcriptomics datasets

· Process raw sequencing data (10x Genomics Cell Ranger, STAR, Velocyto, etc.) and perform downstream analysis (QC, clustering, integration, differential expression, trajectory analysis, cell-cell communication, gene regulatory networks)

· Build large-scale atlases that include multiple species/tissues/conditions (1-10M cells)

· Develop scalable workflows for multi-dataset integration (scVI), metadata management, batch correction, and reproducible data processing

· Perform advanced computational analyses including machine learning, pseudotime inference, RNA velocity, pathway analysis, and ligand-receptor modeling

· Collaborate closely with wet-lab biologists to interpret results and refine experimental hypotheses

· Help maintain code repositories, documentation, and data-management standards

· Prepare figures and data summaries for publications, grant applications, and presentations

Minimum Qualifications

· MS or PhD in Computational Biology, Bioinformatics, Computer Science, Systems Biology, Neuroscience, or related field (or equivalent research experience)

· Demonstrated experience analyzing single-cell omics data using established pipelines

· Proficiency with R and/or Python for bioinformatics workflows (Seurat, Scanpy, scVI, scVelo, CellChat, etc.)

· Familiarity with Linux environments, version control (Git)

· Strong understanding of transcriptomics, genomics, and basic molecular biology concepts

Preferred Skills

· Experience with spatial omics (e.g., Visium, MERFISH, CosMx, MIBI)

· Knowledge of machine-learning methods for high-dimensional biological data and large-scale atlas integration

· Prior experience in neuroscience, computational, or developmental biology

· Ability to communicate results clearly to both computational and experimental audiences

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