241 machine-learning "https:" "https:" "https:" "https:" "https:" "UCL" "UCL" positions in United Arab Emirates
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activities. EOE/AA/Minorities/Females/Vet/Disabled/Sexual Orientation/Gender Identity Employer UAE Nationals are encouraged to apply. Where to apply Website https://www.timeshighereducation.com/unijobs/listing
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fostering academic excellence in learning, research, and teaching. UAE Nationals are encouraged to apply. Where to apply Website https://www.timeshighereducation.com/unijobs/listing/406710/post-doctoral-assoc
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the development of mathematical models for signal transmission and reception, derivation of fundamental performance limits, algorithmic-level system design, and performance evaluation through computer simulations
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access and opportunity; and fostering academic excellence in learning, research, and teaching. UAE Nationals are encouraged to apply. Where to apply Website https://www.timeshighereducation.com/unijobs
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the Job related to staff position within a Research Infrastructure? No Offer Description Description The Deep Learning laboratory in the Division of Science, New York University Abu Dhabi, seeks
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to the excellence and vibrancy of our academic community. Applications are welcome from all qualified candidates. In line with UAE regulations, Emirati candidates are encouraged to apply. About NYU Abu Dhabi https
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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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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 records
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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