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Field
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is looking for an aspiring PhD candidate to research causal machine learning and uncertainty quantification for Earth Observation time-series. Currently, predictive AI in Earth Sciences relies heavily
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applied methodologies in Data and Image Analysis, Computational Imaging, Statistical Learning, Uncertainty Quantification, Robust Estimation, and Deep Neural Networks. The group combines expertise in
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Machine Learning. Profile of the graduate The graduate displays deep theoretical knowledge in molecular and cell biology, genetics and virology, with focus on some specific branch of these scientific fields
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the future of quiet, sustainable flight! Background New aerospace concepts like Urban Air Mobility (UAM) introduce air taxis and delivery drones in cities that are densely populated. The human impact of
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automated vehicles, the design of control center and teleoperation concepts, and sustainable mobility and operational concepts in public transportation. A particular focus is on the technical supervision
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to study the behavior of tissues at single cell resolution. We offer an inspiring intellectual, collaborative, and multidisciplinary research environment to support your career goals and provide access
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preliminary data indicate that in childhood brain tumours, antigen-specific T and B cell responses at the CNS borders are functionally impaired. We hypothesise that dysfunction of the meningeal lymphatic system
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systems increasingly provide personalized recommendations in domains such as nutrition and lifestyle. However, many recommender and prediction systems rely heavily on opaque machine learning techniques
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mimicked with in vivo models of metastasis, which provides unique opportunities to mechanistically dissect what drives the different cell states. You will link clinically relevant phenotypes to putative
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and democratic participation of citizens. You will focus on developing adaptive learning systems that enhance the transparency and contestability of AI decisions through personalized, multimodal