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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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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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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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Thermal Comfort and Cardiovascular Health Model in Smart Homes Using Internet of Things and Deep Learning Supervisor Names: Dr Matias Garcia-Constantino The effects of global warming have intensified in
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significant computational component. We strongly recommend a background in machine learning and coding. Applicants with a background in areas such as computational neuroscience, reinforcement learning, or deep
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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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record and familiarity with the existing literature and research in the field of statistical machine learning, and deep learning or large language models. Possess sufficient specialist knowledge, research
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candidates capable of teaching and research within the area of machine learning, including deep learning, reinforcement learning, and computer vision. We also welcome applicants whose work spans other AI areas