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The Opportunity: Conduct research in Natural Language Processing (NLP), Computer Vision, and Artificial Intelligence (AI), with the goal of publishing in top-tier conferences such as CVPR, ICCV
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oncology-specific NLP models with peer-reviewed publications is preferred. Strong quantitative skills are mandatory, including analysing large-scale databases (e.g., Hospital Authority EHR) using R/STATA/SAS
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. Knowledge of Arabic language is preferred, but not required. Previous work on Arabic NLP is preferred but not required. The terms of employment are very competitive and include housing and educational
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machine learning models, natural language processing (NLP), and ontology-based frameworks to enhance simulation, curriculum development, and personalized learning in health professions education. Develop
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: Artificial Intelligence / Machine Learning Knowledge Representation and NLP methods Clinical Informatics Bioinformatics Biomedical Ontology Public Health Informatics Nursing Informatics Imaging Informatics
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fields Preferred Qualifications: Experience developing and evaluating language models or NLP systems for analyzing user-generated content, including feedback or open-ended survey responses. Familiarity
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 10 hours ago
is available at UNC under Prof. Mohit Bansal in the UNC-NLP Lab. The focus of this NLP+ML postdoc position will include, but is not limited to, multimodal+embodied semantics, human-like language
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, preferably Python and experience with frameworks such as TensorFlow or PyTorch. Mentoring: The primary supervisor, Dr. Hua Xu, is a well-known researcher in clinical NLP. The postdoc will be directly mentored
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associate position with a focus on natural language processing (NLP). This extremely fast-moving and competitive field has produced innovations with highly visible impact in industry, education, and public
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language processing (NLP) to augment clinical decision-making and expand access to high-quality healthcare. Our lab develops new methods to improve model trustworthiness and leverages heterogeneous clinical data