Research Scientist/Engineer 1

University of Washington•Seattle, WA

2d•Onsite

About The Position

This role focuses on computational biology/bioinformatics, specifically utilizing Pixel-seq and an immune receptor-focused multimodal expansion (ImmunoPixel-seq) for spatial transcriptomics and proteomics. The work involves processing Next-Generation Sequencing (NGS) data, mapping spatial barcodes, performing single-cell and spatial analysis, and cell segmentation in various tissues like brain and tumors. The primary goal is to develop and maintain robust pipelines for transforming raw sequencing data into high-quality, spatially resolved single-cell datasets. These datasets will be used for disease-focused analyses to advance mechanistic discovery and translational hypotheses, particularly in areas like the tumor microenvironment and neuroanatomical circuits.

Requirements

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

Nice To Haves

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; interest in medical/MD-PhD pathways.
DevOps-lite: GitHub Actions CI, environment pinning, reproducible reference bundles, and runbooks.
Experience assisting with server upgrades in collaboration with IT (CUDA/cuDNN & GPU driver stacks, Slurm client updates, module systems).
Basic familiarity with configuration/monitoring for research workflows (e.g., Ansible basics, Prometheus/Grafana dashboards) under IT guidance.
Storage and I/O awareness for high-throughput data (scratch NVMe vs. bulk); performance troubleshooting for pipelines.

Responsibilities

End-to-end data processing (BCL/FASTQ → QC → counts) including demultiplexing, adapter/quality trimming, UMI handling, alignment/quantification, and generation of MultiQC reports and run manifests (20%).
Spatial barcode mapping & registration, including building/validating barcode to (x,y) maps for Pixel-seq, error correction, joining gene/protein counts to spatial coordinates, and QA of mapping rates (15%).
Segmentation & QC, applying and benchmarking nuclei or whole-cell segmentation (e.g., Cellpose/StarDist/SAM), and maintaining curated masks and QC thumbnails (20%).
Downstream single-cell & spatial analysis, including creating annotated data objects (e.g., AnnData/Seurat), normalization, clustering, label transfer, spatial neighborhood/domain analysis, and multi-omic modeling for RNA+protein where applicable (20%).
Pipeline automation & reproducibility, implementing and maintaining Snakemake/Nextflow workflows with containers (Apptainer/Docker), CI tests, and clear documentation (10%).
Project support, collaboration & reporting, including preparing figures/tables, writing concise analysis memos, and contributing to methods sections (7%).
Light server/environment maintenance & upgrades (DevOps-lite), building and updating containerized analysis environments, and maintaining conda/uv environments (5%).
DevOps-lite & data stewardship, maintaining analysis environments/containers, writing basic SLURM job scripts, and coordinating with IT on storage/backup hygiene (3%).