Computational Immunology Postdoc

University of California, San Francisco (UCSF)

San Francisco, CA

Job posting number: #7087295

Posted: October 18, 2021

Application Deadline: Open Until Filled

Job Description

Postdoc position in Computational Immunology at UCSF

The Koth (https://sarcoidosis.ucsf.edu/) and the Ansel (https://ansel.ucsf.edu/) Labs are seeking a computationally-focused postdoc or PhD-level research scientist to join a funded collaboration to define the longitudinal immunological host responses to granulomatous inflammation by integration of data from single cell RNA seq, ATAC-seq and deep clinical phenotyping from 288 independent human samples.

Successful candidates would have experience working with large biological datasets, such as single-cell immune system data (single-cell RNAseq, ATAC-seq). Experience with data management, multi-modal datasets, and machine learning/statistics is preferred, along with interest in working collaboratively, and learning from both data scientists and wet-lab biologists. The position is based at the UCSF Parnassus campus located in San Francisco, California.

This position offers a collaborative environment comprised of a computational community and close contact with wet-lab basic and clinical researchers through the CoLabs (https://colabs.ucsf.edu/) and the Koth and Ansel labs. Data for the proposed project will be generated using consistent and highly optimized workflows.

Interested candidates should contact Laura Koth (Laura.Koth@ucsf.edu)

Required qualifications
- PhD in computational biology, bioinformatics, or similar, or PhD in biology field with a substantial computational component in thesis work
- Extensive experience working with high-dimensional biological data and large data sets
- Experience working with some subset of the following: single-cell sequencing data (scRNAseq, CITEseq, etc.), metagenomic sequencing, CyTOF or high-dimensional flow cytometry, proteomics, metabolomics
- Advanced skills in R, Python, or similar
- An interest and aptitude for learning to work with new data types and multi-modal analysis
- Version control (git), command-line tools, pipeline use/development
- Strong abilities in working as a part of a team and collaboration
- Strong skills in communicating results and documentation
Preferred qualifications
- Experience working with clinical/demographic data
- Experience developing methods for analyzing high-dimensional and multi-modal biological data
- Ability to write standalone packages/libraries for use in future projects/biological community






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