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disconnectivity in brain networks relates to symptom networks and recovery trajectories in psychiatric patients. Apply and further develop methods from network science, machine learning, and computational
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, neuroimaging and clinical psychiatry, with direct clinical impact. Your main activities are: analyzing and integrating multimodal MRI data for biotype identification; applying machine learning and advanced
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from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques to uncover
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of the Quantum & Computer Engineering (QCE) department is looking for a highly motivated PhD candidate who is eager to work on AI based solutions for predictive inteligence for MRI scanning. The candidate will
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Learning. Work closely with the other PhDs and researchers on the project towards reaching the overall research goal. Contribute to educational activities (e.g. provide lectures, assist teachers, supervise
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- and machine-learning-based methods that automatically describe and model geodata sources using textual metadata (NLP) and the geodata itself; contribute to a corpus of geo-analytical scenarios with
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driving systems, and new methods for fusing sound with other sensor data for more robust environment perception through deep learning. We offer a fully-funded 4-year PhD position at the Intelligent Vehicles
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the response model from reactive to proactive. The goal is to increase transparency and trust in the DNS namespace. Key research activities will include applying machine learning and graph-based techniques
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(including ultra-high-field and ultrafast MRI) Computational and network neuroscience Machine learning and biologically inspired AI Vision science and predictive coding Clinical neuroscience and
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preferably with data analysis and machine learning (e.g., Python, AI frameworks). You have strong analytical and problem-solving skills, with the ability to translate complex clinical processes into structured