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Better Personalization of Deep Learning-Enhanced Hearing Devices - Royal National Institute for Deaf People (RNID) EPSRC Centre for Doctoral Training in Sustainable Sound Futures PhD Research
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learning. The employment is full-time for two years starting from August 1st 2025 or by agreement. Apply latest April 7th 2025. Project description Geometric deep learning refers to the study of machine
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National Aeronautics and Space Administration (NASA) | Pasadena, California | United States | about 6 hours ago
wildland-urban interfaces— across a wide range of climate conditions. Using machine learning methods, we will optimize the weightings of each contributing factor and identify the key drivers of wildfire risk
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systems on different temporal and spatial scales. For our Research Group Applied Optimization we are looking for a Research Associate / PhD: Physics-informed deep learning for PDE-constrained optimization
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Wee Kim Wee School of Communication and Information Nanyang Technological University, Singapore Part-Time Lecturer for IN6242 Deep Learning Foundations We are seeking a part time lecturer to teach
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The LivePerson Centre for Speech and Language offers a 3 year fully funded PhD studentship to explore novel and innovative learning methods for construction of speech processing models. Domain
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group has implemented state-of-the-art deep learning for underwater communications; deep learning models underwater environment based on real data. Our preliminary study shows that state-of-the-art deep
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Deepfakes, derived from "deep learning" and "fake," involve techniques that merge the face images of a target person with a video of a different source person. This process creates videos where
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project is to harness the latest developments in high-performance computing and deep learning (DL) technologies to address some of the key technical challenges, and finally demonstrate a DL-enabled system
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to frailty assessment could be beneficial. Manual measurements from CT scans, however, are labor-intensive and subject to observer variability. The advent of deep learning in medical imaging presents a