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This is a unified application form for all positions in the Beyesian Deep Learning group at KAUST led by Prof Maurizio Filippone, including Research Intern MS/PhD Student PhD Student Postdoctoral
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While deep learning has shown remarkable performance in medical imaging benchmarks, translating these results to real-world clinical deployment remains challenging. Models trained on data from one
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Discovery Project, this research aims to develop highly novel physics-informed deep learning methods for Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET) and applications in image
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The NYU Entrepreneurial Institute’s Assistant Director of Research Commercialization leads programs and initiatives designed to accelerate the commercialization of deep technology and research from
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professionals. The ideal candidate will bring deep scholarly knowledge with a global perspective to the classroom, enabling students to investigate and interpret the complex relationships between historical
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Engineering in the 2025 QS World University Rankings by Subjects. The EEE Rapid-Rich Object SEarch (ROSE) Lab focuses on research in: (i) visual search & retrieval, (ii) video analytics & deep learning, and
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addition to audio. Candidates will be expected to devise novel multi-modal generation models by incorporating ideas and techniques from various techniques, such as causality, deep learning, deep reinforcement
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of this PhD is to develop physics-informed neural operator frameworks that embed governing equations and invariants of fluid mechanics directly into learning architectures, enabling real-time, generalizable
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for Paediatric Echocardiography Supervisor Names: Dr Patrick McAllister This PhD project will develop and evaluate explainable, domain-adaptive deep learning approaches for image- and video-based classification
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) and her research group! The successful candidate will join a multidisciplinary team working at the intersection of deep learning, cosmology, and high-energy physics, with opportunities to contribute