Historical Text Analysis Postdoc in NLP
3 days ago
Required Skills and Qualifications
- NLP Methodologies: Experience in developing and applying NLP techniques, 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, and topic modeling.
- Historical Textual Data: Familiarity with handling archaic language, obsolete terms, OCR errors in digitized historical documents, and contextualizing historical events and linguistic usage over time.
- Programming Languages: Proficiency in Python, R, or Java, and experience with NLP libraries and frameworks like NLTK, spaCy, TensorFlow, PyTorch, or similar.
- Publication Record: A strong publication record in NLP and computational linguistics conferences and journals.
- Collaborative Environment: Experience working in collaborative, interdisciplinary research environments and an understanding of incorporating perspectives from critical race theory, gender studies, and global history into research on NLP for historical textual data.
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