Print and Probability Research Associate - Dietrich College

Print and Probability Research Associate - Dietrich College

Carnegie Mellon University is a private, global research university that challenges the curious and hardworking to deliver work that matters. Our outstanding institution has distinctive areas of excellence and a culture marked by ambition and a deep, practical engagement with challenges facing society.

From creative writing to statistics and data science, behavioral economics to social and political history, Dietrich College is home to 11 humanities and sciences departments, programs and institutes.

The Print & Probability project seeks a Research Associate to develop AI methods for identifying printers of anonymous early modern books (1450-1800).

Core responsibilities include:

• 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 (other) 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

Qualifications:

• 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)

Preferred Qualifications:

• 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