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Field
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PhD in Computational Imaging for High-Throughput Applications (1.0 fte) As a PhD student, you will be embedded in the research group Computational Imaging and Deep Learning (CIDL), part of
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ensuring interpretability and real-time deployment. The successful candidate will work on cutting-edge AI techniques, combining deep learning, probabilistic modeling, and edge computing to improve
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strong interest in foundational research in the above-mentioned research areas strong programming skills, preferably in Python, including experience with deep learning frameworks (e.g., PyTorch, TensorFlow
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Higher education in computer science, Minimum 25 years of professional activity, minimum 20 years of research experience, member of research contracts focused on deep learning problems for computational
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imaging (crucial) Experience with image segmentation, deep learning, or computer vision. Experience with 3D image processing or inverse problems. Experience with experimental research and data acquisition
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Are you interested in developing mathematically grounded methods for uncertainty quantification in deep learning, particularly for large language models in healthcare applications? Are you looking
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learning and deep learning applied to electroencephalography in the context of brain-computer interfaces, including experience with MATLAB and Python and in the design and conduct of experimental studies
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of the out-of-distribution (OOD) performance of hybrid deep learning architectures. Research in optimisation techniques associated with flat minima in deep neural models. Development and analysis of self
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design and evaluate immersive XR experiences that allow users to safely acquire navigation skills in realistic environments such as public transportation hubs, university campuses, and airports. You will
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or complementing traditional physics-based approaches by data-driven ones, using Machine-Learning (ML). Such approaches allow enormous gains of time, in a way that can be related to the astonishing efficiency