Scientist - Genomics and Proteomics - Apply here!
Chan Zuckerberg Biohub, San Francisco, CA
The Chan Zuckerberg Biohub is a well-funded, non-profit life science research institute in San Francisco, CA. We focus on developing and deploying new technologies to study biological systems and fight diseases, in collaboration with researchers at UCSF, UC Berkeley and Stanford University. At the Biohub, biologists, physicists, engineers and data scientists work together on highly interdisciplinary and collaborative teams to tackle projects at the forefront of modern biological research.
Our Cell Atlas initiative aims to “reverse engineer” the human cell by mapping the details of how the cell is built in order to predictably tune its properties and behavior. As part of this effort, we are characterizing genome-wide libraries of fluorescently tagged cell lines and determining the subcellular localization and interaction partners of every protein in the human genome. We develop and make use of a wide array of methods, including CRISPR-based genome engineering, deep sequencing, customized robotics, mass-spectrometric proteomics, live-cell fluorescence microscopy, and data science.
We are seeking an outstanding scientist to spearhead the analysis and visualization of the genomics and proteomics experiments that are an essential component of this project. You will work collaboratively with our team of engineers and biologists to develop computational pipelines and data management systems that are integrated with upstream wet-lab workflows and downstream analysis. The ideal candidate will be excited about working at the intersection of genome engineering, systems biology, and data science.
Want to combine your passion for cell biology with your love for data visualization, in an open and collaborative environment? Excited to analyze first-hand one of the largest live-cell imaging and protein interaction datasets in the world? Join our team!
- Design and prototype interactive tools to visualize and share genomics and proteomics experiments
- Contribute to developing interactive web apps for the design and analysis of genome-engineering experiments
- Use statistical tools and machine learning methods to extract patterns from large, heterogeneous multi-omics datasets
- Author publications and mentor junior colleagues
- Ph.D. in Bioinformatics, Molecular Biology, Computational Biology, Genome Science or a related field
- Proven track record in managing, analyzing, and visualizing genomics, proteomics, and/or multi-omics datasets
- Solid understanding of statistical and machine-learning methods
- Fluency in Python and familiarity with bioinformatics and data science libraries
- Familiarity with relational database design, web development frameworks, and software development best practices
- Good understanding of CRISPR-based genome editing technologies
- Enthusiastic about systems biology, big datasets, and open-source software
- Highly collaborative and team-oriented with excellent written and oral communication skills
- Experience with cloud-based infrastructure and deep learning frameworks is a plus