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About the project Deep learning could revolutionise character animation by enabling the automatic transfer of poses from observations like 2D images to 3D characters. This project aims to tackle
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PhD Supervisor: Antonia Marcu Supervisory Team: Antonia Marcu, Jonathon Hare Project description: Deep Learning (DL) is a widely successful tool. However, there are many fundamental challenges left
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an opportunity to delve into the area of geometric deep learning within the broader landscape of machine learning and 3D computer vision. As a candidate, you'll have the chance to develop theoretical concepts and
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research in 3D scene analysis and generative diffusion models. Project description This PhD position offers an opportunity to delve into the area of geometric deep learning within the broader landscape
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or mathematical statistics, focusing on geometric deep learning. The position covers four years of third-cycle studies, including participation in research and third-cycle courses. The last day to apply is August
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The Department of Mathematics and Mathematical Statistics is opening a PhD position in mathematics or mathematical statistics, focusing on geometric deep learning. The position covers four years
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Stig Brøndbo 4th June 2024 Languages English English English Faculty of Science and Technology PhD Fellow in deep learning for spatio-temporal medical image analysis Apply for this job See
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to the research program. Job Requirements: A PhD in Computer Science or relevant fields. Strong background in deep learning and multi-modal learning. Strong publication record in top conferences/journals, such as
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]. For these reasons, we aim to go beyond the weaknesses of these methods, by investigating novel classes of generative models with deep learning to address anomaly detection. The goal of this PhD thesis is to explore
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modalities may also lack the required accuracy. Better identification of these cancers in lymph nodes can make diagnosis faster and facilitate improved detection and treatment. Deep learning/AI algorithms have