Computational Biologist & Project Manager in Genomics; Biostatistician

Computational Biologist & Project Manager in Genomics (Biostatistician
2) at Stanford University summary:

This role combines expertise in computational biology and project management to map the regulatory wiring of the human genome, leveraging advanced genomics, machine learning, and CRISPR technologies. The position involves leading collaborative NIH-funded projects, developing predictive models, managing large-scale biological datasets, and mentoring lab members. Based at Stanford University, the role focuses on uncovering genetic mechanisms of disease to advance precision medicine and biomedical innovation.

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, which harbor thousands of genetic risk variants for common and rare diseases and could reveal targets for therapeutics that aim to precisely tune cellular functions — if only we could map the complex regulatory wiring that connects 2 million regulatory elements with 21,000 genes in thousands of cell types in the human body.

The Engreitz and Kundaje Labs have developed new experimental approaches and computational methods that could enable mapping this regulatory wiring at massive scale (see 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 new tools combining single-cell genomics, CRISPR perturbations, and machine learning to assemble regulatory maps of the human genome and uncover mechanisms of complex diseases.

For more information and recent work, see and

Project overview:
We aim to develop and apply computational models to interpret the function of any noncoding variant or protein-coding gene in the human genome, across many human cell types in the body. Toward this goal, we are leading highly collaborative projects in two NIH-funded Consortia:
MorPhiC ( and IGVF ( MorPhiC aims to characterize the functions of genes through CRISPR perturbations and predictive modeling (Adli et al. Nature 2025). IGVF aims to characterize the impact of genomic variation on function by combining single-cell mapping, genomic perturbations, and predictive models (IGVF Consortium, Nature 2024). This position will involve improving predictive models of variants, enhancers, and genes and applying them to large single-cell and CRISPR datasets generated by MorPhiC and IGVF to create a comprehensive catalog of the regulatory wiring of the genome.

We are looking for creative and passionate people at any stage in their careers, including computational biologists, bioinformaticians and software engineers. Candidates will train to lead and design team science computational projects that push the boundaries of genomic technology and reveal the functions of genetic elements associated with human diseases.

Our laboratories are co-located in the Department of Genetics and Biomedical Innovations Building at Stanford University. Our department is a dynamic, interdisciplinary workplace that will provide unique access to cutting edge technologies and scientific thought, with the potential for widespread recognition of scientific contributions. We value a diversity of values, backgrounds, and approaches to solving problems.

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 and 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.

Duties include:

• Apply state-of-the-art machine learning models to large…