Research Scientist/Engineer 1

Summary

The University of Washington is a prestigious institution dedicated to advancing education and research. They are seeking a Research Scientist/Engineer 1 to develop robust pipelines for spatial transcriptomics and related fields, focusing on high-quality data processing and analysis to support disease-focused research.

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/StarDist/SAM); maintain curated masks and QC thumbnails
• Downstream single‑cell & spatial analysis – 20%
• Create annotated data objects (e.g., AnnData/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 (DevOps‑lite) — 5%
• Build and update containerized analysis environments, maintain conda/uv environments
• DevOps‑lite & data stewardship – 3%
• Maintain analysis environments/containers; basic SLURM job scripts; coordinate with IT on storage/backup hygiene

Skills

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

Qualifications

Must Haves

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

Benefits

• This position is eligible for H-1B sponsorship.
• For information about benefits for this position, visit https://www.washington.edu/jobs/benefits-for-uw-staff/