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/forschung/gruppen/numerical-analysis/research/ Typical responsibilities you can expect: Mathematical derivation, analysis, and comparison of models, methods, and simulation approaches Formal proofs, e.g
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of climate model output by means of classical statistical and machine-learning methods #coordination of scientific workflows among project partners Your profile #Master's degree and PhD degree in meteorology
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degree and PhD degree in meteorology, oceanography, physics or mathematics, with a strong interest in the application of statistical and data analysis methods excellent knowledge in UNIX/Linux and Unix
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, probabilistic models Representation learning, self-supervised learning, foundation models Data analysis, non-linear statistics, knowledge management Your profile PhD in Computer Science, Bioinformatics
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scaling and generalization behavior Roll out the model to the global user community Requirements PhD or MSc in computer science, physics, mathematics or a related discipline Experience with large-scale HPC
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master's degree (or equivalent diploma) and a PhD in meteorology, oceanography, or a related natural or geoscientific discipline with significant physical and mathematical components. It is essential
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teaching and curriculum development. Your qualifications PhD in computer science, data science, applied mathematics, physics, or a related field. Strong expertise in machine learning and deep learning
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' prognosis or treatment decisions. For modeling, we use both public and proprietary clinical and research data greatly enriched by our own repository of digital pathology images. A further focus lies on
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focus on deep 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
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. For modeling, we use both public and proprietary clinical and research data and generate our own repository of digital pathology images. A further focus of our lab is the improvement of digital pathology