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Machine Learning or Biodiversity Genomics (2 Positions)

Smithsonian Institution, Washington, District of Columbia

Position Description:
The Smithsonian Institution Data Science Lab, housed within the Office of the Chief Information Officer is looking to fill 2 postdoctoral fellowships in Washington, DC:

  1. focused on applying machine learning tools to digitized collections data
  2. focused on developing bioinformatics tools for biodiversity genomics

The Data Science Lab was recently formed in response to the dramatic increase in all forms of digital data across the Smithsonian (19 museums, 9 research centers and zoo) . We seek to build collaborations both across the Smithsonian units as well as with universities and outside agencies. Some of the first papers coming out of our group include a robber-fly genome:, and the application of deep learning to digitized herbarium specimens
The successful applicants will be able to design their own projects that fit into the scope of the Data Science Lab.

Applicants should possess a Ph.D. in a biological or computational discipline, demonstrate a strong publication record and ability to conduct independent research. All applicants are expected to develop strong written and communication skills.
Desirable qualifications:

  1. Machine learning postdoc: Proficiency in Python and familiarity with Python machine learning libraries such as TensorFlow and PyTorch. Interest in how we can apply these tools to diverse collections data, such as digitized herbarium specimens.
  2. Biodiversity genomics postdoc: Proficiency in genome assembly, annotation, and phylogenomic methods as well as a standard programming language such as Python. Experience using an HPC. Interest in non-model organism genomics.

To Apply:
Please submit a curriculum vitae, a 1-page statement of research interests, and contact details for 2-3 academic references to Rebecca Dikow (
Review of applications will begin immediately and continue until the positions are filled.