92 machine-learning "https:" "https:" "https:" "https:" "https:" "https:" "https:" positions in United Arab Emirates
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Science, or Information Science, or a bachelor’s degree with several years of experience and expertise in their field. The position requires experience with at least one of the following: Data Science, Machine Learning
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Science, or Information Science. The position requires experience with at least one of the following: Data Science, Machine Learning, Computational Social Science, Big Data. Relevant skills could include statistical analysis, data
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particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module that relies on machine learning has been developed and we want to take that module
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teaching faculty to teach an undergraduate course, Machines that Create, an introductory yet comprehensive overview on Generative AI and Foundation Models, covering the methods and techniques driving modern
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at the intersection of artificial intelligence and cultural heritage. The successful candidate will be involved in cutting-edge research and development in 3D computer vision and machine learning for the digital
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-relationships, materials optimization, materials under extreme conditions, and generative AI. Candidates must possess substantial experience in artificial intelligence and machine learning methods, specifically
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computational biophysics Machine learning and data analysis for biological systems Biomedical imaging and signal processing Molecular modeling and simulations AI applications in bioinformatics or health sciences
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developing new machine learning methodologies that tackle unique computational problems in healthcare applications. We use large real-world complex datasets, including data extracted from electronic health
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of this project is to add support for automatic code optimization in Tiramisu. In particular, we want to use machine learning/deep learning to achieve this. Currently, a basic automatic optimization module
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research team working at the intersection of machine learning, algorithmic fairness, human-computer interaction, and responsible AI. The project aims to investigate how bias emerges in data pipelines and AI