Computational Scientist, Lung Transplant Immunology

About the role

The UCSF Lung Transplant Research Laboratory, led by Principal Investigators Calabrese and Greenland, is seeking a computational scientist to contribute to fundamental and mechanistic research in transplant immunology and airway biology. This role offers opportunities for clinical translation and collaboration across various disciplines.

Responsibilities

• Lead end-to-end analysis of multimodal genomic datasets, from raw data through biological interpretation.
• Define and pursue scientific questions, shaping hypotheses with the PI and collaborators, designing analyses, and translating findings into publications.
• Build durable, reproducible pipelines that can be used by future researchers and published as part of our methods.
• Co-design experiments with wet-lab bench scientists to ensure data quality and statistical validity.
• Contribute to grant applications and resubmissions, including writing analytic sections and generating preliminary data.
• Mentor graduate students and postdocs on computational best practices; lead lab meetings.
• Represent the lab at national and international conferences.

Requirements

• Specialists appointed at the junior rank must possess (or in process of obtaining) a baccalaureate degree or at least four years of research experience.
• Specialists appointed at the Assistant rank must possess (or in process of obtaining) a master’s 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 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 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 (SLURM).
• Demonstrated reproducible-analysis practice: Git/GitHub, environment management (conda/mamba/renv), and workflow tooling (Nextflow or Snakemake).
• Statistical fluency: Dimensionality reduction, GLMs, mixed-effects models, multiple-testing, and survival analysis, longitudinal modeling, and causal inference.
• Voice 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 rigor and discretion that requires.

Qualifications

• Hands-on experience with one or more of: single-cell multi-omics integration (CITE-seq, scATAC), spatial transcriptomics (Visium/Xenium/CosMx/MERFISH), TCR/BCR repertoire analysis.
• Bioinformatics neural network (AI) expertise.
• Experience with cloud (AWS/GCP) and academic HPC (UCSF Wynton, AWS HealthOmics) at production scale.
• Familiarity with modern ML for genomics.
• Experience analyzing paired mouse & human studies.
• Track record contributing to grants, public data deposition or open-source software.

Preferred Qualifications

• Experience in immunology, transplantation, pulmonary medicine, fibrosis, or related translational settings.
• Experience with bioinformatics neural networks (AI).
• Experience with cloud (AWS/GCP) and academic HPC (UCSF Wynton, AWS HealthOmics) at production scale.
• Familiarity with modern ML for genomics.
• Experience analyzing paired mouse & human studies.
• Track record contributing to grants, public data deposition or open-source software.

Scientific Traits and Collaborative Qualities

• Curiosity about the underlying biology.
• Rigor and humility about negative results, batch effects, confounders, and reproducibility.
• Generosity as a collaborator.
• Strong written and verbal communication skills, including the ability to explain a method to a clinician at the bedside and a biological inference to a statistician.