Research Scientist: Spatial Genomics & Single‑Cell

Field of Research:

Computational biology/bioinformatics with emphasis on spatial transcriptomics, proteomics, and immunosequencing using Pixel‑seq and an immune receptor‑focused multimodal expansion (“Immuno Pixel‑seq”). Work includes NGS data processing, spatial barcode mapping, single‑cell & spatial analysis, and cell segmentation in brain, tumor, and other tissues.

Job Description

Field of Research:

Computational biology/bioinformatics with emphasis on spatial transcriptomics, proteomics, and immunosequencing using Pixel‑seq and an immune receptor‑focused multimodal expansion (“Immuno Pixel‑seq”). Work includes NGS data processing, spatial barcode mapping, single‑cell & spatial analysis, and cell segmentation in brain, tumor, and other tissues.

Purpose Of The Research Project(s)

To build and operate robust, reproducible pipelines that transform raw sequencing + barcode map into high‑quality, spatially resolved single‑cell datasets; to deliver disease‑focused analyses that advance mechanistic discovery and translational hypotheses (e.g., tumor microenvironment, neuroanatomical circuits).

Duties And Responsibilities

End‑to‑end data processing (BCL/FASTQ → QC → counts) – 20%

Demultiplexing, adapter/quality trimming, UMI handling, alignment/quantification; generation of MultiQC reports and run manifests.

Spatial barcode mapping & registration – 15%

Build/validate barcode→(x,y) maps for Pixel‑seq; error correction; join gene/protein counts to spatial coordinates; QA of mapping rates.

Segmentation & QC – 20%

Apply/benchmark nuclei or whole‑cell segmentation (e.g., Cellpose/Star Dist/SAM); maintain curated masks and QC thumbnails.

Downstream single‑cell & spatial analysis – 20%

Create annotated data objects (e.g., Ann Data/Seurat); normalization, clustering, label transfer; spatial neighborhood/domain analysis; multi‑omic modeling for RNA+protein where applicable.

Pipeline automation & reproducibility – 10%

Implement/maintain Snakemake/Nextflow workflows with containers (Apptainer/Docker), CI tests, and clear documentation.

Project support, collaboration & reporting – 7%

Prepare figures/tables; concise analysis memos; contribute to methods sections.

Light server/environment maintenance & upgrades (Dev Ops‑lite) — 5%

Build and update containerized analysis environments, maintain conda/uv environments.

Dev Ops‑lite & data stewardship – 3%

Maintain analysis environments/containers; basic SLURM job scripts; coordinate with IT on storage/backup hygiene.

Visa Eligibility

This position is eligible for H-1B sponsorship.



Minimum Requirements

Bachelor's Degree in CS, Applied Math, Bioinformatics, Computational Biology, ECE and one year of relevant experience with Computational biology/bioinformatics.

Equivalent education and/or experience may substitute for minimum qualifications except when there are legal requirements, such as a license, certification, and/or registration.

Additional Requirements

• Programming & data:
  Python (numpy/pandas), basic R (Seurat/tidyverse), bash;
  Git;
  Linux.
• NGS data processing: BCL→FASTQ demultiplexing; adapter/quality trimming; UMI handling; QC with MultiQC; alignment/quantification to reference.
• Spatial omics:
  Pixel‑seq barcode→(x,y) mapping concepts; creation of spatially annotated objects (Ann Data/Seurat).
• Segmentation:
  Practical use of Cellpose/FICTURE (or similar); basic image QC.
• Single‑cell & spatial analysis:
  Normalization, clustering, label transfer; spatial neighborhood/domain analyses (e.g., with Squidpy/Giotto).
• Reproducibility & automation:
  Snakemake or Nextflow; containerization (Apptainer/Docker); clean documentation; basic SLURM job submission.
• Communication:
  Clear writing of READMEs, short analysis memos, and figure captions for collaboration with biologists/clinicians.
• Linux/HPC usage;
  Slurm job submission, resource requests, and environment management.

• Probabilistic modeling: scVI/scANVI/totalVI for RNA and RNA+protein integration.
• GPU experience:
  PyTorch/CUDA for segmentation/model inference.
• Data stewardship: DVC or equivalent data versioning; basic dashboarding/monitoring (Prometheus/Grafana).
• Domain breadth:
  Prior coursework/research in biochemistry or genetics;…