Postdoctoral Researcher in Language Model Interpretability and Digital Humanities

Your position

You will lead the project's computational work, co-supervised by the PI and the project partner Dr. Juan Luis Gastaldi (Sorbonne University and ETH Zürich). Your tasks include:

• preparing and validating the historical corpora (OCR quality, genre and date metadata) with the project's research assistant
• fine-tuning open-weight language models on period texts (continued pretraining with parameter-efficient methods such as LoRA)
• building the attribution pipeline, with token-level SHAP checked against probing and causal-intervention methods
• running systematic model tests and controlled prompting experiments with the team's literary historians
• leading the technical side of the benchmark release (documentation, baselines, public leaderboard)
• leading or co-authoring papers at NLP and machine-learning venues and co-authoring the project's humanities publications

The position supports your own research profile and further academic qualification.

Your profile

• A doctorate (by the time of appointment) in computer science, NLP or computational linguistics, machine learning, digital or computational humanities, or a related field with a substantial computational component
• Hands-on experience training, fine-tuning and analysing transformer language models in Python (PyTorch, Hugging Face), including on GPU clusters
• Experience with interpretability methods (feature attribution, probing, activation patching, sparse autoencoders) and/or with building evaluation datasets and benchmarks
• Experience designing experiments with proper controls, baselines and statistical care, and reproducible research-software practice
• A serious interest in historical texts, literary theory and the philosophy of language, and in reading model outputs closely alongside literary historians
• Strong English; knowledge of German is welcome

Desirable: experience with historical or OCR-derived text or semantic change detection; annotation studies; released datasets or open-source tools; publications in NLP or ML venues; engagement with literary theory, historical materialism, hermeneutics, or structuralist or historical linguistics.