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The project will prioritise digitising these records using natural language processing (NLP) and machine learning (ML) to create structured datasets. These will support AI applications in paediatric care
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learning, deep learning, and LLM-based methods to multimodal clinical datasets e.g. EHR, imaging, omics, sensor data Designing and implementing NLP pipelines for clinical text processing, semantic annotation
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and NLP for software; publish at top venues and contribute to open-source artifacts Design, implement, and evaluate prototypes that relate bug reports to code, collect lightweight runtime evidence, and
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and implementing NLP pipelines for clinical text processing, semantic annotation, and representation learning Developing embedding-based representations of clinical variables and documents to support
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implementation of complex coding schemes (e.g., participation & belonging, inclusion, recognition, justice) in NLP pipelines: from guidelines/definitions to label formats to model and evaluation design. Setting up
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and green AI. Use cases in administrative, educational, and legal domains. The successful candidate will become part of the Odense NLP group under the Centre for Machine Learning . The centre is part of
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applications for a PhD candidate focusing on the use of natural language processing (NLP) methods to advance climate impact research. The overarching goal is to develop a benchmark dataset on the socioeconomic
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, learner-aware sequencing of content. This includes work on semantic parsing, structured NLP, graph-based neural models, metacognitive prompting, ontology alignment across disciplines, and human-in-the-loop
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Basic data analysis and processing skills Statistical analysis knowledge and expertise MS Excel, MS Access, SQL, SAS Python, web-scraping and NLP Any programming experience is a plus Willingness to learn
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. Argument(ation) mining, the new and rapidly growing area of Natural Language Processing (NLP) and computational models of argument, aims at the automatic recognition of argument structures in large resources