Print and Probability Research Associate - Dietrich College

Carnegie Mellon University•Pittsburgh, PA

3d•Onsite

About The Position

Carnegie Mellon University's Dietrich College is seeking a Research Associate for the Print & Probability project. This role will focus on developing AI methods to identify printers of anonymous early modern books (1450-1800). The project builds on previous work identifying clandestine printers of famous historical works and will integrate large language models with computer vision to uncover hidden networks of controversial printing during periods of censorship. The position emphasizes practical problem-solving, interdisciplinary learning, and collaboration within a research-intensive environment.

Requirements

Master's degree required.
1-3 years of research experience required.
Demonstrated expertise with large language models (fine-tuning, prompting, deployment).
Strong Python programming with deep learning frameworks (PyTorch, TensorFlow).
Experience with unstructured historical data (text extraction, entity resolution, knowledge graphs).
Excellent communication skills and commitment to interdisciplinary collaboration.
Evidence of scholarly productivity (publications, presentations, software).
A combination of education and relevant experience from which comparable knowledge is demonstrated may be considered.

Nice To Haves

Knowledge of early modern European history (1450-1800) or book history.
Experience with historical bibliography or archival research.
Familiarity with computer vision for document analysis.
Multilingual reading ability (e.g., English, Latin, French, Spanish, Italian, Dutch).
Publication record in digital humanities or computational social science.

Responsibilities

Develop LLM-driven knowledge graphs that construct probabilistic historical priors from bibliographic records, trial transcripts, censorship lists, and apprenticeship data.
Design agentic frameworks using In-Context Learning and Chain-of-Thought prompting for transparent historical inference.
Develop Historical Hypotheses in collaboration with expert humanists and book historians.
Integrate top-down LLM hypotheses with established bottom-up vision pipeline (existing: dhSegment/Eynollah line extraction, damage detection models, 280M+ character image database).
Assist in original research on clandestine printing networks using computational tools.
Contribute to publications in both AI and humanities venues (machine learning conferences and book history journals).
Contribute to open-source tools and datasets for the research community.
Other duties as assigned.