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Field
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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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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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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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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
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software or similar languages and experience with modern machine learning and deep learning frameworks parallel computing using clusters like UPPMAX and GPUs for high-performance computing and parallel
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and experience with modern deep learning frameworks (e.g. PyTorch) Solid background in machine learning, ideally with experience in NLP, large language models, or sequence modeling Interest in clinical
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, mixed-effects modeling, Bayesian methods, deep learning, variational autoencoders, generative AI). Is an experienced programmer in R and/or Python, and used to working with large datasets and reproducible
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, linear algebra, probability theory, (Bayesian) statistics, optimization and elementary graph theory Familiar with machine learning and deep learning Programming experience (Python or Julia) and their
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the subsequent years, you will combine these insights with predictive modelling and select, train, and validate process-based or deep-learning fire propagation models using a.o. detailed information about