Computational Biologist

6 days left

Location
California, United States
Posted
19 May 2020
End of advertisement period
19 Jul 2020
Ref
85729
Academic Discipline
Life sciences, Biological Sciences
Contract Type
Permanent
Hours
Full Time

The Department of Genetics is seeking a computational biologist (Research Data Analyst 1) to join the Engreitz Laboratory to map the regulatory wiring of the human genome to discover genetic mechanisms of heart diseases. The Engreitz Lab will launch at the Stanford University Department of Genetics and Children’s Heart Center starting in 2020. The ideal candidate will join as soon as possible. Candidate will have the option to train with the Engreitz group at the Broad Institute in Cambridge, MA during a transition period in 2020.

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. bioRxiv 2019). 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 computational biologists. Candidates will train to lead and design experimental and/or computational projects that push the boundaries of genomic technology and reveal 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 www.engreitzlab.org
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 candidate for this position should have expertise in statistical methods in data analysis, preferably with applications to high-throughput sequencing or other biological assays; enthusiasm for using computational approaches to learn fundamental mechanisms of gene regulation and understand human disease; fluency in Unix, programming, and bioinformatics tools (Python, R, or equivalent); excellent communication, organization, and time management skills; and be a creative, organized, motivated team player.

Duties include:

  • Collaborate with experimentalists and computational biologists to develop and apply functional genomics techniques to understand gene regulation and the genetic basis of heart disease
  • 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

* - Other duties may also be assigned.

DESIRED QUALIFICATIONS:

  • Suggested: B.S. in computational biology, computer science, physics, statistics, math, molecular biology, or related field. 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
  • Enthusiasm for using computational approaches to learn fundamental mechanisms of gene regulation and understand human disease
  • Excellent communication, organization, and time management skills
  • Creative, organized, motivated, team player

EDUCATION & EXPERIENCE (REQUIRED):

Bachelor's degree or a combination of education and relevant experience. Experience in a quantitative discipline such as economics, finance, statistics or engineering.

KNOWLEDGE, SKILLS AND ABILITIES (REQUIRED):

  • Knowledge of Unix, programming, and data analysis tools (Python, R, or equivalent)
  • Strong writing and analytical skills.
  • Ability to prioritize workload.

CERTIFICATIONS & LICENSES:

None.

PHYSICAL REQUIREMENTS*:

  • Sitting in place at computer for long periods of time with extensive keyboarding/dexterity.
  • Occasionally use a telephone.
  • Rarely writing by hand.

* - 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.

WORKING CONDITIONS:

  • Some work may be performed in a laboratory or field setting.
  • Additional WORKING CONDITIONS: (remove if none)

WORK STANDARDS (from JDL):

  • 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, http://adminguide.stanford.edu.

Additional Information

Schedule: Full-time
Job Code: 4751
Employee Status: Regular
Grade: G
Department URL: http://genetics.stanford.edu/
Requisition ID: 85729

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