Senior NLP Researcher for Atlantic Colonial History

3 days ago


Copenhagen, Copenhagen, Denmark Københavns Universitet Full time
Postdoctoral Position in Natural Language Processing

The Saxo Institute at the University of Copenhagen invites applications for a postdoctoral researcher position in the field of Natural Language Processing (NLP) with a focus on multilingual historical texts from 1600 to 1900. Our team works to advance our understanding of diachronic development of gendered and racialized language in historical documents produced between 1600 to 1900.

About the Project

We are seeking candidates with a strong background in NLP and a keen interest in applying their expertise to collaborative historical research on Atlantic colonial history. The successful candidate will join an interdisciplinary research team in the context of the DFF Sapere Aude project Racialized Motherhood .

The primary role of the postdoctoral researcher will be to develop and implement NLP methodologies that can aid the team in effectively retrieving, processing, analyzing, and interpreting relevant historical documents. These documents are available as scans of printed originals and were written in several languages.

Responsibilities and Requirements
  • Develop and apply NLP methodologies to historical texts in several languages.
  • Collaborate with historians, computational linguists, or digital humanities researchers to ensure the successful integration of NLP techniques into historical research.
  • Publish research findings in peer-reviewed journals and present at international conferences.

To be eligible for this position, you must have a Ph.D. in Computer Science, Computational Linguistics, Computational Social Science, or a related field with a focus on NLP. You should demonstrate expertise in key NLP methodologies, including text preprocessing, OCR post-correction, named entity recognition, part-of-speech tagging, syntactic and semantic parsing, machine learning approaches, deep learning techniques, information retrieval and extraction, sentiment analysis, topic modeling, and interpretable NLP.

Familiarity with historical textual data and willingness to tackle the challenges specific to this domain, such as handling archaic language and obsolete terms, dealing with OCR errors in digitized historical documents, contextualizing historical events and linguistic usage over time, and ethically handling racialized and gendered language is also required.

You should be proficient in Python and other relevant programming languages such as R or Java, and have experience with NLP libraries and frameworks like NLTK, spaCy, TensorFlow, PyTorch, or similar. A strong publication record in NLP and computational linguistics conferences and journals is expected.

About the Team

Our team works in an interdisciplinary environment and welcomes candidates who are enthusiastic about overcoming the challenges imposed by historical textual data. We are committed to incorporating perspectives from critical race theory, gender studies, and global history into research on NLP for historical textual data.



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