The University Library System Information Technology Residency Program is a 24-month, full-time, paid, benefits-eligible residency designed to give new graduates of Pitt’s School of Computing and Information a practical career-building opportunity which benefits both the Resident and the ULS. The Resident will gain directed, hands-on experience building out ULS information systems within a mutually chosen set of specialization tracks.
The ULS seeks to support academic success through our collections, services, and spaces. We are committed to our vision of encouraging intellectual activity and the creation, dissemination, preservation, and celebration of knowledge and creative expression and see the library as a place where important conversations happen, curiosity and experimentation are encouraged, collaboration is key, and diversity and inclusion are fundamental. We are also committed to preserving the past and present work of our communities as a node in a vast network of global knowledge.
The Applicant must be:
An Applicant will be a preferred candidate when possessing demonstrated experience with one or more of the following:
The interview and selection process will consist of:
The Residency will feature hands-on work in real-life problem solving across various information technology domains. The Resident will receive mentoring and supervision by seasoned professionals within the ULS. The Residency experience will help the Resident enhance the practical technical and professional skills which will prepare the Resident for future pursuits within an Information Technology career. Since the ULS is substantially invested in leveraging and contributing to open source, the Resident will accumulate a portfolio of published, peer-reviewed work with application far beyond the ULS itself.
The Resident will have the opportunity to explore multiple specialization tracks in rotations lasting from 3 to 12 months. Annually, the Resident and the ULS will identify two or more specialization tracks of mutual interest, from among:
Leverage machine learning and artificial intelligence to enhance the processing and publication of user-sourced data, historic documents, and scholarly works.
Example: Implement a workflow which allows training models for AI-assisted OCR / HTR on historic primary source documents. Evaluate toolsets and train OCR on scholarly or archival corpuses, generating fulltext indexing of the original work as well as machine learning assisted metadata creation. Develop a workflow in which non-technical users can refine the training and reprocess existing texts.
Improve decision making capability and develop evaluation and assessment resources though the utilization of multiple and diverse data sources.
Example: Assess the value of our collection and identify potential collection gaps by examining whether works cited in our faculty’s publications are represented in our collections. Gather works published by Pitt researchers via APIs from Web of Science, Scopus, Symplectic Elements, etc. Harvest and clean citations from published works. Check for local holdings via the library management system SRU. Visualize results by subject area.
Enhance orchestration and deployment architecture to abstract data from application from architecture and to seamlessly provision high availability production infrastructure and on-demand development infrastructure.
Example: Leverage orchestration and deployment tools to standardize and simplify the build, configuration, and monitoring of 80 production and development VMs. Analyze and classify deployed applications to segment the application, configuration, and data for rapid deployment, testing, and recovery. Build out redundant architecture to create high availability service of data, middleware, and frontend systems.
Expose 50TB of Digital Content to the world by developing enhancements to the indexing and delivery of content, from static images to streaming audio and video and beyond.
Example: Transform high quality preservation objects into user-accessible derivatives by using APIs to deliver the content to the user's choice of device, regardless of the user's ability/barriers. Implement systems for streaming audio / video with transcripts, for 3D interactions or virtual reality.
Contribute to international open source development projects, implementing international technical standards and connecting diverse APIs.
Example: Evaluate NISO recommendations such as PIE-J or SUSHI/COUNTER against the Public Knowledge Project's scholarly publishing applications. Spec, develop, test, and pull request code changes into the open source project to implement these standards.
Extend interactive digital exhibition space beyond the walls of the exhibition room; link realtime building data to customize the user experience of visitors.
Example: Develop a web front end for existing curated exhibition content current displayed within an interactive wall, exposing that same content experience to users via a browser. Integrate various data sources to enable wayfinding, meetups, and discovery of open computers or study space within Hillman Library, all within the user's mobile device.
Analyze existing and prospective systems to implement a workflow enhancements and efficiency improvements.
Example: Perform a systems analysis of existing workflow and systems used to catalog, select, digitize and publish archival special collections, identifying opportunities in which to collect and enhance metadata descriptions of those collections, such as allowing efficient Structural and Semantic metadata collection at the item level for Archival collections. Evaluate existing or prospective toolsets to enable this work by staff and design and implement a workflow using the proposed toolset(s).
To apply, see the formal posting of this Residency in the Pitt Talent Center. Recruitment for a July 2024 start date opened in April 2024.
Questions and Comments may be sent to:
Clinton Graham via ctgraham@pitt.edu
