Post-Doctoral Research Fellow - Functional Genomics And RNA Biology

  • Full-Time
  • Seattle, WA
  • Fred Hutchinson Cancer Research Center
  • Posted 3 years ago – Accepting applications
Job Description
Overview: Cures Start Here. At Fred Hutchinson Cancer Research Center, home to three Nobel laureates, interdisciplinary teams of world-renowned scientists seek new and innovative ways to prevent, diagnose and treat cancer, HIV/AIDS and other life-threatening diseases. Fred Hutch’s pioneering work in bone marrow transplantation led to the development of immunotherapy, which harnesses the power of the immune system to treat cancer. An independent, nonprofit research institute based in Seattle, Fred Hutch houses the nation’s first cancer prevention research program, as well as the clinical coordinating center of the Women’s Health Initiative and the international headquarters of the HIV Vaccine Trials Network. Careers Start Here.
At Fred Hutch, we believe that the innovation, collaboration, and rigor that result from diversity and inclusion are critical to our mission of eliminating cancer and related diseases. We seek employees who bring different and innovative ways of seeing the world and solving problems. Fred Hutch is in pursuit of becoming an antiracist organization. We are committed to ensuring that all candidates hired share our commitment to diversity, antiracism, and inclusion.


The Subramaniam lab is part of the Basic Sciences Division and the Computational Biology Program at Fred Hutch in Seattle. Members of the lab come from a variety of scientific backgrounds including physics, biochemistry, and microbiology. The lab places a strong emphasis on one-on-one mentoring and on providing a supportive environment for learning and research. Trainees are prepared for a wide range of careers in academia and industry through learning of broadly applicable technical and organizational skills. We encourage applicants from historically underrepresented groups and from any disadvantaged socio-economic backgrounds. More information on the Subramaniam lab can be found by clicking this link.
The goal of this postdoc research project is to use functional genomic approaches to discover and characterize novel mechanisms of mRNA translation in human cells. Our longer-term goal is to identify how these mechanisms are dysregulated across different tissue types and disease states. Thanks to methods such as ribosome profiling and RNA-seq, we have unprecedented genome-wide snapshots of gene expression at single nucleotide resolution. However, our ability to interrogate and predict the functional effects of mRNA sequence motifs on gene expression and cell physiology has been limited. We aim to address this gap using:
  • Pooled deep sequencing methods to measure in parallel the effects of thousands of mRNA sequence motifs on gene expression in human cells.
  • Unbiased CRISPR screens to identify factors that act at specific motifs.
  • Machine learning and kinetic models to quantitatively predict the effect of novel mRNA motifs on gene expression.
  • Biochemical and genetic studies to test the resulting models of gene regulation.
Responsibilities: The postdoctoral fellow will learn and apply cutting-edge deep sequencing approaches to identify novel sequence motifs and protein factors regulating mRNA translation in human cells. They will use targeted biochemical and genetic approaches to characterize the identified motifs and factors. They will learn and use computational methods to analyze large-scale sequencing data and formulate quantitative models.
The fellow will have the opportunity to explore and bring novel approaches and ideas to their project by collaborating with other labs at Fred Hutch, including:
  • Melody Campbell (Structural Biology using cryo-EM)
  • Andrew Hsieh (Cancer Biology)
  • Adam Geballe (Virology)
The fellow will present their work at group meetings and conferences, and write up their work for publication.Qualifications:
  • A PhD is required, preferrably in biochemistry, molecular biology or related field
  • The ideal candidate for this project will have PhD-level expertise in applying biochemical and structural approaches to study RNAs and proteins or in mammalian RNA biology.
  • Candidates with strong expertise in cell biological approaches or cutting edge microscopy are also welcome to apply.
  • The candidate will be highly motivated to learn deep sequencing methods and computational approaches for studying gene regulation.
  • The candidate should be able to work independently and must have strong communication skills.

Application instructions:To apply, please submit the following materials
  • Your CV including contact information for 2 references.
  • A statement of your research interests. This should specifically address why your background and interests are a good fit for this project.
Graduate students getting ready to complete their PhD are encouraged to apply.
A statement describing your commitment and contributions toward greater diversity, equity, inclusion, and anti-racism in your career or that will be made through work at Fred Hutch is requested of all finalists. Our Commitment to Diversity: We are proud to be an Equal Employment Opportunity (EEO) and Vietnam Era Veterans Readjustment Assistance Act (VEVRAA) Employer. We are committed to cultivating a workplace in which diverse perspectives and experiences are welcomed and respected. We do not discriminate on the basis of race, color, religion, creed, ancestry, national origin, sex, age, disability (physical or mental), marital or veteran status, genetic information, sexual orientation, gender identity, political ideology, or membership in any other legally protected class. We are an Affirmative Action employer. We encourage individuals with diverse backgrounds to apply and desire priority referrals of protected veterans. If due to a disability you need assistance/and or a reasonable accommodation during the application or recruiting process, please send a request to our Employee Services Center at hrops@fredhutch.org or by calling 206-667-4700.
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