127 machine-learning-"https:" "https:" "https:" "https:" "https:" "https:" "U.S" research jobs at NEW YORK UNIVERSITY ABU DHABI
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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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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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, seeks a Post-Doctoral Associate or a Research Associate to join a lab focused on applied machine learning. The successful applicant will participate in research involving human computation, knowledge
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/Females/Vet/Disabled/SexualOrientation/Gender Identity Employer UAE Nationals are encouraged to apply Where to apply Website https://www.timeshighereducation.com/unijobs/listing/408278/post-doctoral-assoc
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to apply Website https://www.timeshighereducation.com/unijobs/listing/408037/post-doctoral-assoc… Requirements Additional Information Work Location(s) Number of offers available1Company/InstituteNEW YORK
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soft robot-assisted simulations in the areas of brain machine interaction, wearable haptics, and rehabilitation. The successful applicant will have the following technical experience in: PhD degree in
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cover letter, curriculum vitae with full publication list, statement of research interests, at least two reference letters and a transcript, all in PDF format. Please visit our website at https
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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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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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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