Please share the news widely that we are looking to fill two Digital Humanities Postdoctoral Fellowships: https://datascience.si.edu/digital-humanities-postdoctoral-fellowships
Digital humanities postdoctoral fellowships
The Smithsonian Institution Data Science Lab (datascience.si.edu) housed within the Office of the Chief Information Officer (SI-OCIO) in Washington, DC, is seeking two postdoctoral fellows to conduct independent research and digital humanities scholarship in collaboration with the United States Holocaust Memorial Museum and the Smithsonian American Women’s History Initiative.
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 a zoo). We seek to build collaborations both across Smithsonian units, as well as universities and other institutions. Members of our group work on a variety of data-intensive research topics, including biodiversity genomics and machine learning applications of digitized museum collections.
Collaboration with the United States Holocaust Memorial Museum (USHMM):
We are seeking a postdoctoral fellow to lead a project to improve the discoverability of the United States Holocaust Memorial Museum digitized collections with the use of machine learning tools. We are particularly interested in applying deep learning techniques to classify unknown document types and natural language processing techniques to delve into document contents, with all project components leading to interactive visualizations of results. The scope, however, can be molded to fit the fellow’s interests.
Collaboration with the Smithsonian American Women’s History Initiative (AWHI):
The Smithsonian American Women’s History Initiative (AWHI) launched in 2018 following a Congressional report that concluded the Smithsonian is the natural place to honor women’s contributions to the nation. AWHI will illuminate women’s pivotal roles in building and sustaining our country and will expand what we know of our shared history with an emphasis on better representation of diverse histories. With a digital-first mission, the initiative will use technology to amplify women’s voices, reaching millions of people in Washington, D.C., across the nation, and around the world.
We are seeking a postdoctoral fellow to lead a project to build tools and visualizations to illuminate data on American women’s history for researchers and the general public, and to improve the discoverability and utility of the data held across the Smithsonian. We are particularly interested in applying deep learning and other machine learning tools to surface new stories on American women’s history. The scope, however, can be molded to fit the fellow’s interests.
Given the interdisciplinary nature of these projects, applicants should possess a PhD with a relevant interdisciplinary focus. This may include either social sciences and humanities disciplines (e.g., history, sociology, Judaic studies, women’s studies) with a research focus in digital data or technical disciplines (e.g., computer science, NLP) with a research focus on historical document analysis. Applicants should demonstrate a strong publication record and the ability to conduct independent research. Strong written and communication skills are also required. Applicants should be proficient in Python, familiar with machine learning frameworks such as PyTorch or TensorFlow, and have experience with a High-performance Computing Cluster. The fellow will be advised by both Smithsonian AWHI, and/or USHMM staff with expertise in the technical and historical subject matter, respectively.
Appointment is initially for one year, with the possibility for renewal. Stipend is $60,000 per year, with an additional $5,000 health insurance offset. Please contact Rebecca Dikow at [log in to unmask] with questions. To apply, submit a curriculum vitae, a 1-page statement of research interests, and contact details for 2-3 academic references to [log in to unmask] by April 15, 2019.
Rebecca B. Dikow
Research Data Scientist
Data Science Lab
Office of the CIO