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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | 4 days ago
to the development and validation of computational methods that integrate images with other multimodal data. The postdoctoral research associate will design, implement, and evaluate algorithms and models
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University of North Carolina at Chapel Hill | Chapel Hill, North Carolina | United States | about 1 hour ago
to the development and validation of computational methods that integrate images with other multimodal data. The postdoctoral research associate will design, implement, and evaluate algorithms and models
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the UCD School of Mathematics and Statistics. The successful candidate will lead research focused on developing a scalable, AI-driven quality control (QC) system for synoptic and climate observation data
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development Qualifications PhD degree in Bioinformatics, Computational Biology, Computer Science, Mathematics, Physics, or a related field Strong experience with programming in Python, R, or similar languages
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Are you interested in the empirical foundations of flood damage modelling and keen to translate that knowledge into practical strategies for insurers to reduce losses through early warning and
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/Qualifications We are looking for a researcher with a PhD in physics, astronomy, applied mathematics or a closely related discipline, completed before the starting date. Essential qualifications: The candidate
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As a postdoctoral researcher, your primary responsibilities will be: Develop machine learning and deep learning models, with a strong focus on computer vision, for the characterisation and
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and Computer Science at the University of Basel invite applications for a PhD or postdoctoral position focused on Large Language Models (LLMs), including pre-training and post-training. The successful
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across a range of application areas, including education and healthcare. As these systems are increasingly deployed in high-stakes environments, there is a growing need for machine learning models
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: to develop next-generation autonomous crop monitoring and decision-support systems for Controlled Environment Agriculture. By integrating plant sensing, data and crop models, we aim to enable more precise and