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information, applying hydrological models to simulate extreme floods, and contribute to recommendations for practice on flood design estimation. Your responsibilities: Develop and apply statistical methods
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and rigorous analytical modeling to answer foundational questions in supply chain finance, with links to resilience and sustainability, aiming at publication in leading journals. About us Our team
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Alfred-Wegener-Institut Helmholtz-Zentrum für Polar- und Meeresforschung | Bremerhaven, Bremen | Germany | 2 months ago
hydroacoustics, bioinformatics and/or image analysis (ZooScan, UVP) Excellent analytical skills, including statistical modelling, mathematical modelling and network analysis Proficiency in coding (R, Python
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models and analysis pipelines for real-time performance, enabling adaptive imaging and feedback control Design active-learning and retraining strategies to robustly generalize to new microbial communities
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are particularly important to us. Gender equality is an important aspect for us. To support work life balance we offer flexible working hours, variable part-time, job-sharing models and participation in mobile work
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, for event reconstruction and classification, including potentially machine learning/AI Interpretation in suitable theoretical models Contribution to software activities that are required for wider use by DESY
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Helmholtz Association of German Research Centres | Oldenburg Oldenburg, Niedersachsen | Germany | 3 months ago
documents, for example from the Prize Papers project; the creation of knowledge graphs using language models; and the automated analysis of knowledge graphs using methods from network science and cultural
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networks for solving inverse problems, learning robust models from few and noisy samples, and DNA data storage. The position is in the area of machine learning, with a focus on deep learning for inverse
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14.12.2022, Academic staff The BMBF-funded position is part of the CoMPS project, which is a multidisciplinary project combining the fields of mathematics, computer science, geophysics, and high
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are modeled using information theory. We wish to investigate how interleaving can reduce the overhead and computational load due to coding coefficients required in classical linear random network coding