Computational Scientist, Lung Transplant Immunology

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 creates 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 lung transplant research group is well established and funded through a balanced portfolio of federal, VA, foundation, and industry mechanisms. 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 (Department of Medicine, Department of Microbiology and Immunology, ImmunoX, CoLabs, Bakar ImmunoX Initiative), 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
• Bulk transcriptomes and metagenomes linked to detailed clinical phenotypes
• 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 our 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

• Specialists appointed at the junior rank must possess (or in process of obtaining) a baccalaureate degree (or equivalent degree) or at least four years of research experience (e.g., with instrumentation and research equipment, social science research methods, or creative activities).
• Specialists appointed at the Assistant rank must possess (or in process of obtaining) a master’s degree (or equivalent degree) or a baccalaureate degree with 3 or more years of research experience.
• Specialists appointed at the Associate rank must possess (or in process of obtaining) a master’s degree (or equivalent degree) or five to ten years of experience in the relevant specialization.
• Specialists appointed at the full rank must possess (or in process of obtaining) a terminal degree (or equivalent degree) 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 (eg. SLURM).
• Demonstrated reproducible‑analysis practice:
  Git/Git Hub, environment management (conda/mamba/renv), and workflow tooling (Nextflow or Snakemake).
• Statistical fluency:
  Dimensiona…