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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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project include two aspects: (1) based on the cutting-edge technologies from deep learning, computer vision or physics-informed machine learning, develop robust surrogate forward models to predict
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depends on the background of a suitable candidate. The main topics of the group in the past few years were generative modeling, 3D reconstruction, image-editing, and deep learning using 3D data. More
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computer science, Data Science, Computational Science, Artificial Intelligence, or a related field. Strong academic foundation in machine learning, deep learning, and AI fundamentals. Required Skills Technical
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frontier in hydrothermal research in a young ocean basin at the intersection of geology, tectonics, hydrothermal processes, deep-sea ecology, and mineral resources. A new interdisciplinary KAUST-funded
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The VCC center at KAUST is looking for research scientists in Prof. Wonka's research group. The topics of research are computer vision, computer graphics, and deep learning. A suitable candidate
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findings into actionable detection content • Develop hypotheses and use data analytics to validate or refute threat scenarios • Document threat hunting activities, findings, and lessons learned Detection
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and management, machine and deep learning, as well as a solid understanding of wave phenomena and geophysical data analysis and imaging.
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membrane performance. Therefore, this objective of this research is develop efficient algorithms and models based on deep learning to accelerate the physics simulation for membrane relevant processes, which
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work primarily focuses on applications of AI, machine learning, deep learning, or data science should demonstrate a significant record of original mathematical research to be considered within the scope