Professional Researcher – Krummel lab

Full Professional Researcher – Krummel labThe Krummel lab in the Department of Pathology seeks a computational scientist to help lead the UCSF Custom Immunoprofiler project (co-led by the Krummel and Combes labs at UCSF), applying single-cell genomics and machine learning to patient tumor samples and to in vivo and ex vivo perturbation models in murine and human tumors to identify, prioritize, and validate therapeutic targets.

The work builds on our immune archetype framework of discrete tumor microenvironment states hypothesized to represent distinct tumor ecosystems that predict therapeutic response.

Key Responsibilities:

Lead the computational strategy and analysis for the Custom Immunoprofiler projects, applying single-cell genomics and machine learning to patient tumor samples to identify and prioritize therapeutic targets.

Oversee and perform end-to-end scRNA-seq analysis, from raw data (FASTQ) through standardized pipelines into analysis-ready objects in Seurat (R) or Scanpy (Python), with rigorous quality control, normalization, and integration.

Apply and extend consensus non-negative matrix factorization (cNMF) and related approaches to define gene expression programs and characterize their variation across patients and experimental conditions.

Interpret results in biologically and clinically meaningful ways, and assemble analysis packages and reports suitable for review by the group and external stakeholders and collaborators.

Propose and develop new analytical initiatives and methods, including machine learning approaches for therapeutic target identification and prediction of treatment response.

Partner closely with experimental and clinical investigators to design and interpret in vivo and in vitro perturbation experiments that validate computational predictions.

Work with project leads to co-mentor and train junior bioinformaticians, and establish best practices for reproducible workflows, documentation, and scalable computational pipelines.

Required Qualifications:

Ph.D. (or equivalent degree) in bioinformatics, computational biology, computational immunology, genomics, or a related field, with two or more years of post-degree experience, by the time of hire. Demonstrated ability to independently lead computational analyses and interpret results in close collaboration with experimental and clinical teams.

Strong proficiency in R and/or Python.

Deep, demonstrated experience analyzing single-cell RNA-seq data with Seurat and/or Scanpy.

Experience developing or applying machine learning methods to genomic or biological data.

Experience with high-performance computing environments and reproducible workflows.

Applicants’ materials must list current and/or pending qualifications upon submission.

Preferred Qualifications:

Hybrid expertise spanning experimental and computational approaches (preferred, not required).Track record of close collaboration with experimentalists (highly preferred).Experience mentoring and training junior staff.

Experience with single-cell spatial transcriptomics.

Familiarity with tumor immunology, the tumor microenvironment, and/or patient class discovery.

See Table 13B for the salary range for this position. A reasonable estimate for this position is $141,200 - $257,100.  to apply.