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of this WASP-financed project is machine learning, in particular dealing with generative models and instabilities associated with cycles of retraining on mixtures of human and machine-generated data
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qualifications required for employment as associate professor. The Computer Vision Laboratory (CVL) is looking for an assistant professor in machine learning with a focus on motion analysis from video. CVL is a
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of Machine Learning and Computational Chemistry. This recruitment is linked to the Wallenberg AI, Autonomous Systems and Software Program (WASP) and Wallenberg Initiative Materials Science for Sustainability
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, both over the wireless interface and within the core network, will be driven by AI and machine-learning applications. This research will develop efficient communication strategies to support
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approach that integrates wireless communication, computer vision, and machine learning to optimize PC transmission from sensors to an edge server for remote registration. The research is funded by Wallenberg
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documented experience in mathematical modeling of biological systems or in deep learning/machine learning, from education or previous positions. Experience in both of these areas is meriting for the position
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application! Work assignments Subject area: Computational studies of the influence of microstructural features on the structural integrity of metallic materials using machine learning Subject area description
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data
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application! Work assignments Within the framework of the current position, you will teach at the undergraduate and advanced levels in design, production engineering, and production systems, with a focus on
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are looking for candidates with a PhD in Computer Science, Visualization and Media Technology, Machine Learning or a closely related research field. A strong background in machine learning and visual data