Computational Scientist, Lung Transplant Immunology

Computational Scientist, Lung Transplant Immunology – Lung Transplant Research Laboratory (UCSF Specialist series, Assistant – Full level)

The UCSF Lung Transplant Research Laboratory (PI’s Calabrese and Greenland) is recruiting a computational scientist who is excited to address fundamental, mechanistic questions in transplant immunology and airway biology, with clear paths to clinical translation. Lung transplantation provides a uniquely informative human setting because of the defined mismatch between host and donor genetics, predictable peri‑operative injury, and reproducible immune perturbations. Outcomes from lung transplantation lag behind other solid organs, amplifying the potential for high‑impact discovery with immediate consequences for patient outcomes.

We are looking for a scientist who will own the analysis from question to inference.

The lab studies innate immune inflammation, NK cell biology, airway epithelial responses, and chronic tissue remodeling across primary graft dysfunction (PGD), acute and chronic lung allograft dysfunction (ALAD/CLAD), antibody‑mediated rejection, and parallel injury phenotypes in cystic fibrosis and ARDS. The successful candidate will be embedded in one of the largest lung‑transplant biorepositories in the country and in the UCSF immunology ecosystem, a uniquely deep environment for immune‑focused discovery.

• Longitudinal single‑cell (Chromium 5/3, CITE‑seq, TCR/BCR) atlases of bronchoalveolar lavage and small‑airway brushings
• Spatial transcriptomics (Visium HD, Xenium/Cos Mx) of lung tissue
• Bulk transcriptomes and metagenomes linked to detailed clinical phenotypes
• Mechanistic mouse model data
• Primary human airway epithelial cells differentiated at air‑liquid interface data
• Integration of these data with multimodal flow cytometry, microbiome, plasma proteomics, EHR‑derived outcomes, and increasingly with ML approaches

• Lead end‑to‑end analysis of multimodal genomic datasets, from raw data through biological interpretation, with ownership of methodology.
• Define and pursue scientific questions: shape hypotheses with the PI and collaborators, design analyses, and translate findings into figures, talks, and manuscripts.
• Build durable, reproducible pipelines that can be re‑run by the next trainee and published as part of methods.
• Co‑design experiments with wet‑lab bench scientists to ensure data are statistically defensible and biologically interpretable.
• Contribute to grant aims and resubmissions, including writing analytic sections and generating preliminary data.
• Mentor graduate students, postdocs on computational best practices; lead lab‑meeting.
• Represent the lab at national and international conferences.

• At the junior rank: baccalaureate degree (or equivalent) or at least four years of research experience.
• At the Assistant rank: master’s degree (or equivalent) or a baccalaureate degree with 3 or more years of research experience.
• At the Associate rank: master’s degree (or equivalent) or five to ten years of experience in the relevant specialization.
• At the full rank: terminal degree (or equivalent) or ten or more years of experience in the relevant specialization.
• First‑author or major‑contribution publication(s) using bulk RNA‑seq, scRNA‑seq, or comparable high‑dimensional modality.
• Strong working proficiency in R (Bioconductor, Seurat or equivalent) and Python (scanpy, anndata, scikit‑learn, pandas).
• Expertise in Linux‑based high performance computational environments (e.g., SLURM).
• Demonstrated reproducible‑analysis practice:
  Git/Git Hub, environment management (conda/mamba/renv), and workflow tooling (Nextflow or Snakemake).
• Statistical fluency: dimensionality reduction, GLMs, mixed‑effects models, multiple‑testing, survival analysis, longitudinal modeling, and causal inference.
• Vibe Coding for efficiency (Visual Studio).
• Excellent scientific writing and communication; ability to explain methods to clinicians and biology to engineers.
• Commitment to working with IRB‑governed human samples and clinical metadata with the required rigor and discretion.

• Hands…