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representations. In this project, you will substantially improve quantitative magnetic resonance imaging (MRI) image quality using deep learning approaches. Quantitative MRI allows healthcare providers
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quality that is not visible in blood or clinical characteristics. By combining the results of AI-driven image analysis of histological samples conducted in this PhD project with biomarker data and outcome
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how intrinsic plasticity contributes to memory encoding and alters cognitive processes. In this project, we will leverage the advanced voltage imaging in larval zebrafish to investigate how intrinsic
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candidate you will: Collaborate closely with the experimental team that acquires these multiscale data Analyse rat MRI data, including integration with histology and calcium imaging Combine data across
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holders, setting requirements on data quality and measurement conditions. Together with cardiologists, supervisors and colleague PhD students, you will further perform human testing of several prototype
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that has been generated in a prior laboratory-setting project. Specifically, we will integrate recent advances in artificial intelligence-based automated interpretation of medical images, and new knowledge
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Quantitative MRI is a PICNIK with Physics-Informed data Consistent Neural Implicit K-space representations. In this project, you will substantially improve quantitative magnetic resonance imaging
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holders, setting requirements on data quality and measurement conditions. Together with cardiologists, supervisors and colleague PhD students, you will further perform human testing of several prototype
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rehabilitation center, and will be embedded in the Vision and Imaging Data Analytics group at the Department of Intelligent Systems and Centre for Cognitive Science and Artificial Intelligence at Tilburg
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rapidly evolving retrieval paradigm where generative models are used to directly generate document identifiers given a user query. This paradigm departs from traditional multi-stage retrieval pipelines and