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aspiring computer science researcher interested in deep neural networks? Do you want to understand the fundamental limits of machine learning? Then you have a part to play as a PhD candidate. By
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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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methodological advancements for the deep learning analysis of spectroscopy signals and focus on metabolic biomarkers of interest for psychiatric disorders. The thesis will be carried out mainly at the CREATIS
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and machine learning Deep understanding and rich hand-on experience in deep networks Good communication skills in English Background knowledge in computer vision and/or point cloud processing. Prior
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University of Massachusetts Medical School | Worcester, Massachusetts | United States | about 11 hours ago
electronic health records and clinical text through machine (deep) learning modeling and natural language processing, the incumbent will also contribute to software package development, scientific paper
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captioning. Development of causal reasoning tools, including causal inference, counterfactual analysis, causal discovery. Development of deep learning methods and theory. Development of computer vision methods
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analysis. Profile Essential You obtained a Master's in Computer Science, Artificial Intelligence, Bioinformatics, Physics, Engineering, Bio-engineering, or equivalent. Background in AI (deep learning
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Positions: We have an opening for a PhD position in the areas of machine learning and computer vision. The position is supported financially by a SNSF project, whose aim is to build controllable
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/computational mathematics, engineering, biostatistics, statistics, and hands-on demonstrated experience in deep learning or a PhD in any of the previously mentioned fields. Work Week: Standard (M-F equal number