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Field
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Demonstrated record of publications in relevant computer science venues, such as NLP conferences, machine learning conferences, and/or general interest journals Application Requirements Document requirements
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Essentials PhD (completed or near completion) in Computer Science, Computer Vision, NLP, Machine Learning, Computer Graphics/Animation, HCI, or a related field. Strong background in deep generative
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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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language processing (NLP), and fine-tuning techniques Familiarity with structured reasoning, chain-of-thought processes, and agent-based systems is beneficial Strong programming skills (preferably Python); experience
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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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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 22 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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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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, 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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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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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