105 phd-in-computer-vision-and-machine-learning Postdoctoral positions in United States
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technologies, in coordination with academic and industrial collaborators. Qualifications Applicants must hold a PhD degree in electrical/electronics engineering, telecommunications or related field. Other
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research and experimental data in accordance with established protocols. Assist in statistical and comparative analysis of experimental data using appropriate computer software. Contribute to the preparation
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Institute faculty, in areas such as: * Machine Learning and Computer Vision * Natural Language Processing and Data Science * Biomedical Informatics and Computational Neuroscience * Mathematical/Theoretical
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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understanding of artificial intelligence applications and methodologies, such as working knowledge of generative AI tools, use of large language models, machine learning, and ethical frameworks for AI
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reinforcement learning and machine vision. Experience with ROS and the ROS ecosystem Special Requirements: Applicants cannot have received their PhD more than five years prior to the date of application and must
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to support research groups associated with Institute faculty, in areas such as: ● Machine Learning and Computer Vision ● Natural Language Processing and Data Science ● Biomedical Informatics and
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to support research groups associated with Institute faculty, in areas such as: ● Machine Learning and Computer Vision ● Natural Language Processing and Data Science ● Biomedical Informatics and
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 1 day ago
Models (LLMs) and clinical data analysis. About the Position Our Postdoctoral Research Program is designed for candidates who have completed their PhD within the last two years and have experience as
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, with a focus on building multimodal AI models to predict dental caries progression. The successful candidate will work on developing deep learning and computer vision models using longitudinal dental