Computational Scientist, Lung Transplant Immunology

University of California, San Francisco; San Francisco (CA); On-site; USD 63,500 - 194,800; Full time

Job summary

University of California, San Francisco (UCSF) seeks a Computational Scientist for the Lung Transplant Immunology Lab to own end-to-end analysis of multimodal datasets and translate findings into actionable insights.

The role sits within a premier translational immunology environment and involves mentoring, grant contributions, and presenting at conferences.

Qualifications

• First-author or major-contribution publication 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/GitHub, 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 rigor and discretion.

Responsibilities

• Lead end-to-end analysis of multimodal genomic datasets, from raw data through biological interpretation.
• Define and pursue scientific questions with the PI and collaborators; design analyses and translate findings into figures 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 and postdocs on computational best practices; lead lab meetings.
• Represent the lab at national and international conferences.

Skills

R (Bioconductor/Seurat)

Python (scanpy/anndata)

Linux HPC (SLURM)

Reproducible analyses (Git/GitHub)

Data science statistics (GLMs, mixed,

Education

Terminal degree (PhD or equivalent)

Master's degree (or equivalent)

Bachelor's degree + 3+ years research

Tools

GitHub

Conda/mamba/renv

Nextflow

Snakemake

R and Python tooling

Job description

Position Description
Computational Scientist, Lung Transplant Immunology — Lung Transplant Research Laboratory (UCSF Specialist series, Assistant to Full level commensurate with experience)

Position Description
Computational Scientist, Lung Transplant Immunology — Lung Transplant Research Laboratory (UCSF Specialist series, Assistant to Full level commensurate with experience)

The lab and the scientific mission

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.

Ongoing projects and datasets you would work on

• Longitudinal single-cell (Chromium 5′/3′, CITE-seq, TCR/BCR) atlases of bronchoalveolar lavage and small-airway brushings
• Spatial transcriptomics (Visium HD, Xenium/CosMx) 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

Primary Responsibilities

• 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

Required Qualifications

• 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/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.
• 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 rigor and discretion that requires.

Preferred 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
• Prior work in immunology, transplantation, pulmonary medicine, fibrosis, or related translational settings.
• Experience analyzing paired mouse & human studies.
• Track record contributing to grants, public data deposition or open-source software.

Scientific traits and collaborative qualities we look for

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

Appointment level, compensation, and location

Title and step will be calibrated to the candidate's training and experience within the UCSF Specialist series (Assistant, Associate, or Full Specialist). The position is based in San Francisco at UCSF Parnassus and the San Francisco VA Medical Center.

See Table 24B for the salary range for this position. A reasonable estimate for this position is $63,500-$194,800.

Document Requirements

• Curriculum Vitae - CV must clearly list current and/or pending qualifications (e.g. board eligibility/certification, medical licensure, etc.).
• Cover Letter - A cover letter (½–1 page) describing why this role and lab fit your scientific trajectory.
• Statement of Research - A 1–2 page research/methods statement describing one analysis you led end-to-end — the question, the data, your approach, the biological inference, and what you would do differently.
• Statement of Teaching (Optional)

Reference Requirements

• 3-5 required (contact information only)

As a University employee, you will be required to comply with all applicable University policies and/or collective bargaining agreements, as may be amended from time to time. Federal, state, or local government directives may impose additional requirements.

The University of California is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, age, protected veteran status, or other protected status under state or federal law.

UCSF is committed to welcoming and serving all people, honoring the dignity of every individual without preference or prejudice, in support of its public mission and in alignment with our PRIDE values and Principles of Community.

As a condition of employment, the finalist will be required to disclose if they are subject to any final administrative or judicial decisions within the last seven years determining that they committed any misconduct.

• “Misconduct” means any violation of the policies or laws governing conduct at the applicant’s previous place of employment, including, but not limited to, violations of policies or laws prohibiting sexual harassment, sexual assault, or other forms of harassment, or discrimination, as defined by the employer.
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San Francisco, CA