Computational Biologist & Project Manager in Genomics; Biostatistician

Overview

Computational Biologist & Project Manager in Genomics (Biostatistician
2) — School of Medicine, Stanford, California, United States. Information Analytics. Requisition #107087. The Engreitz and Kundaje Labs are seeking a Computational Biologist / Project Manager (Biostatistician
2) to join the Department of Genetics to map the regulatory wiring of the human genome to discover genetic mechanisms of disease. The position is open, and a successful candidate could join immediately.

Lab overview: DNA regulatory elements in the human genome harbor thousands of genetic risk variants for common and rare diseases and could reveal targets for therapeutics that aim to precisely tune cellular functions. The goal is to map the regulatory wiring that connects millions of elements with genes across many cell types. The Engreitz and Kundaje Labs have developed new experimental approaches and computational methods to enable this at scale, with prior work including Fulco et al.

Nature Genetics 2019, Schnitzler & Kang et al. Nature 2024, Avsec et al. Nature Genetics 2021, Pampari et al. bioRxiv 2024. We invent tools combining single-cell genomics, CRISPR perturbations, and machine learning to assemble regulatory maps of the genome and uncover mechanisms of complex diseases. For more information, see https://(Use the "Apply for this Job" box below). and .

Project overview:
Develop and apply computational models to interpret the function of noncoding variants or protein-coding genes across many human cell types. We lead collaborative projects in two NIH-funded consortia:
MorPhiC () and IGVF (https://). MorPhiC characterizes gene functions through CRISPR perturbations and predictive modeling (Adli et al. Nature 2025). IGVF characterizes the impact of genomic variation on function by combining single-cell mapping, genomic perturbations, and predictive models (IGVF Consortium, Nature 2024). The role involves improving predictive models of variants, enhancers, and genes and applying them to large single-cell and CRISPR datasets to create a comprehensive catalog of regulatory wiring.

We seek creative and passionate people at any career stage, including computational biologists, bioinformaticians, and software engineers. Candidates will train to lead and design team science computational projects that push genomic technology boundaries and reveal functions of genetic elements tied to human diseases. Our laboratories are co-located in the Department of Genetics and Biomedical Innovations Building at Stanford University. The department is a dynamic, interdisciplinary workplace offering access to cutting-edge technologies and a culture that values diversity of backgrounds and approaches.

The ideal candidate should have expertise in bioinformatics and computational biology workflows; statistical methods in data analysis with applications to high-throughput sequencing or other biological assays; fundamentals of software engineering; strong knowledge of molecular biology and genomics; fluency in Unix and standard programming/data analysis languages (Python, R, or equivalent); interest in mentoring and training other lab members in computational biology and statistics;

excellent communication, organization, and time management skills; and creativity and motivation.

• Apply state-of-the-art machine learning models to large datasets, including single-cell and Perturb-seq datasets
• Interpret model performance and results
• Develop standards and pipelines to expand analyses across datasets
• Interface with collaborators at Stanford and collaborating labs to design and produce methods and data analysis products
• Track and manage contributions by lab members to consortium activities
• Design and implement generalizable algorithms and tools for analysis of biological data, including high-throughput functional genomics assays
• Evaluate and recommend new emerging technologies, approaches, and problems
• Create scientifically rigorous visualizations, communications, and presentations of results
• Contribute to generation of protocols, publications, and intellectual property
• Maintain and organize computational infrastructure and resources

Note:

Other duties may also be assigned.

• Required:
  
  M.S. or Ph.D. in computational biology, genetics, computer science, statistics, math, molecular biology, or related field, or equivalent practical experience. Talented applicants of all levels are encouraged to apply.
• Demonstrated expertise in statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays
• Experience with data analysis and management, workflow management
• Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent), and a programming language (C/C++, Java)
• Strong knowledge of molecular biology and functional genomics
• Ability to mentor and train other lab members in…