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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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] Subject Areas: mathematical modeling, statistics, machine learning, data-driven modeling, dynamical systems, optimization Appl Deadline: 2026/04/01 04:59 AM UnitedKingdomTime (posted 2026/02/19 05:00 AM
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and machine learning. Dr. Liu's research interests lie in modeling the rapidly-accumulating big data (e.g., muti-omics) in biology and medicine for precision medicine via a variety of statistical and
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to create interactive learning experiences. Career Readiness Competencies Take initiative to learn new tools, systems, or procedures on the job. Present ideas or updates in a clear and organized manner. Ask
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for 10-12 weeks. Responsibilities: Collect and organize different datasets. Derive summary statistics of those datasets. Help with the implementation of machine learning models. Conduct literature