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Senior Computational Biologist

Grade I Job Code 5522 Employee Status Regular Schedule Full-time Requisition ID 87463

The Department of Genetics at Stanford University is seeking a Senior Computational Biologist / Research Scientist (Biostatistician 2) to join the Engreitz Laboratory to map the regulatory wiring of the human genome to discover genetic mechanisms of heart diseases. The Engreitz Lab launched at Stanford in 2020 and is part of the Department of Genetics and the Basic Science and Engineering (BASE) Initiative of the Moore Children’s Heart Center. 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. We have recently developed new approaches that could enable mapping this regulatory wiring at massive scale (see Fulco et al. Science 2016, Fulco et al. Nature Genetics 2019, Nasser et al. Nature 2021, Bergman et al. Nature 2022). We invent new tools combining experimental and computational genomics, biochemistry, molecular biology, and human genetics to assemble regulatory maps of the human genome and uncover biological mechanisms of heart disease.

We are looking for creative and passionate people at any stage in their careers, including senior computational biologists. Candidates will design and lead computational projects that push the boundaries of computational genomics, reveal fundamental aspects of genome regulation, and identify the functions of genetic variants associated with human diseases.

Specific projects include: to leverage computational modeling to understand molecular mechanisms of gene regulation; to chart enhancer-promoter regulation in every cell type and state in the developing heart using large-scale single-cell CRISPR and epigenomic data; to develop computational algorithms and methods to analyze data from new experimental technologies developed in the lab; and develop novel computational models to understand genetic regulation of heart disease by noncoding variants. For more information and recent work, see

The Engreitz Laboratory 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 statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays; 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:

  • Collaborate with experimentalists and computational biologists to develop and apply functional genomics techniques.
  • 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.
  • Design and lead independent projects.
  • 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.

* - Other duties may also be assigned.


  • Suggested: 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
  • Strong knowledge of molecular biology and genomics; wet-lab experience a plus
  • Fluency in Unix, standard bioinformatics tools (Python, R, or equivalent), and a programming language (C/C++, Java)
  • Mentor and train other lab members in computational biology and statistics
  • Excellent communication, organization, and time management skills
  • Creative, organized, motivated, team player
  • A passion for science and sense of urgency to find new medicines to benefit patients


Master's degree in biostatistics, statistics or related field and at least 3 years of experience.


  • Proficient in at least two of R, SAS, SPSS, or STATA.
  • Skills in descriptive analysis, modeling of data, and graphic interfaces.
  • Outstanding ability to communicate technical information to both technical and non-technical audiences.
  • Demonstrated excellence in at least one area of expertise, which may include coordinating studies; statistical methodology such as missing data, survival analysis, statistical genetics, or informatics; statistical computing; database design (e.g., expertise in RedCAP or MySQL); graphical techniques (e.g., expertise in Illustrator).




  • Frequently perform desk based computer tasks, seated work and use light/ fine grasping.
  • Occasionally stand, walk, and write by hand, lift, carry, push pull objects that weigh up to 10 pounds.

* - Consistent with its obligations under the law, the University will provide reasonable accommodation to any employee with a disability who requires accommodation to perform the essential functions of his or her job.


May work extended or non-standard hours based on project or business cycle needs.


  • Interpersonal Skills: Demonstrates the ability to work well with Stanford colleagues and clients and with external organizations.
  • Promote Culture of Safety: Demonstrates commitment to personal responsibility and value for safety; communicates safety concerns; uses and promotes safe behaviors based on training and lessons learned.
  • Subject to and expected to comply with all applicable University policies and procedures, including but not limited to the personnel policies and other policies found in the University's Administrative Guide,

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