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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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the complex multiscale nonlinear interactions at the origin of such extreme events. In this project, you will develop machine learning-based reduced-order models which can accurately forecast
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, scale and resolution in which in vivo pathways of immune cells can be unraveled. Furthermore, it provides a goldmine for training causal machine learning models to move towards precision medicine
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strengths of the University of Tübingen in Computer Sciences and Machine Learning. Potential research directions include, but are not limited to, phylogenetic, demographic, ecological and biogeographic
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/10.1016/j.xcrp.2022.101112 and https://doi.org/10.1080/08940886.2022.2114716 key words synchrotron radiation; X-ray Absorption Spectroscopy, machine learning, artificial analysis, autonomous experimentation
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practical tools deployable in real-world clinical settings. This work is central to a multidisciplinary collaboration bringing together experts in neuroscience, machine learning, and clinical informatics
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analysis and processing: NumPy, Pandas, SciPy; - Machine learning/AI: Scikit-learn, TensorFlow, PyTorch (preferred); - Data visualization: Matplotlib, Seaborn, Plotly. LanguagesFRENCHLevelGood
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of computational methods that enable machines to perform tasks requiring perception, learning, reasoning, and decision-making. It encompasses core areas such as machine learning, data-driven modeling, intelligent
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theory, Machine learning and multivariate statistics, Application in neuroscience, climate research, economics, ...). COBRA is a part of the Department of Complex Systems of the Institute
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Postdoctoral Researcher in Natural Language Processing and Digital Humanities (18 months, full-time)
Python Documented experience with NLP and computational text analysis Experience working with language modelling, semantic analysis, or related machine learning approaches Requirements Documented