Postdoc Position: Explainable AI Cultural Heritage

Job Overview

Do you want to explore how Explainable AI can support cultural heritage institutes and ensure that AI innovations can make a meaningful societal impact? If so, we invite you to apply for this Postdoctoral Researcher position.

As a postdoctoral researcher, you will conduct research focused on questions that are related to the mission of the National Library of the Netherlands. You will also contribute to the supervision of a PhD candidate working on XAI and metadata creation, including both image and text analysis. You will organise benchmarks that assess AI technologies, particularly in the domain of newspapers, both current and historic.

Further, you will contribute to joint initiatives with other project partners, which aim to improve the explainability of AI approaches and connect the development of AI technology to the needs of the cultural heritage sector and society as a whole.

The postdoc project is part of the larger HAICu project, a Netherlands-wide initiative on AI and cultural heritage. In the HAICu project, AI researchers, Digital Humanities researchers, heritage professionals, journalists and engaged citizens collaborate to realise scientific breakthroughs in accessibility and contextualisation of massive multimodal digital heritage collections. The challenges of these collections offer a unique opportunity to take AI to the next level.

Future AI technologies should be applicable outside laboratories and be able to learn from sparse examples, at the same time learning continuously from users. The technology of HAICu pays attention to present-day societal demands with respect to responsible and explainable approaches to multimodal narratives based on the Netherlands’ rich cultural heritage collections.

The postdoctoral project will focus on developing Explainable AI (XAI) technologies tailored to the CH domain, and in particular on the collections at the National Library. The XAI techniques will not only enhance understanding of AI-driven decisions but also support informed decision-making by explicitly expressing the limitations and uncertainties inherent in automated analysis. Ultimately, this work will contribute to more responsible and context-aware applications of AI in preserving and interpreting cultural heritage.