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Fritz Haber Institute of the Max Planck Society, Berlin | Berlin, Berlin | Germany | about 1 month ago
skills and experience and interest in data analysis, data science, machine learning and process automation would be an advantage. Previous experience with XAS or other synchrotron-based techniques would be
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of machine learning and health sciences, with unique access to experimental and clinical data. Embedded in Munich’s thriving AI landscape, fellows benefit from world-class facilities, interdisciplinary
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. Furthermore, we develop advanced scattering methods and machine learning tools for data analysis. For more information, see www.soft-matter.uni-tuebingen.de Qualification and skills Candidates should have good
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culturing, integrating multiple automated subsystems with image-based machine learning models. Our objective is to enable robotic decision-making through machine learning, paving the way for a standardized
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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or a related discipline A solid background in climate and atmospheric sciences, and extreme weather ideally supported by knowledge of machine learning and time series analysis is of advantage, as is
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well as access to state-of-the-art scientific facilities. You can also have a virtual tour of our campus . THE POSITION We invite you to apply for a Postdoctoral Position on Deep Convection (W-0044 | all genders
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omics, environmental, and chemical data, using machine learning and explainable AI. Depending on your background, interests, and evolving project needs, your work may focus on one of these areas or bridge
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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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to generate reproducible, micrometer-scale controllable, and cost-efficient disease models by bringing together experts in molecular systems engineering, machine learning, biomedicine, and disease modeling