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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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Max Planck Institute of Molecular Physiology, Dortmund | Dortmund, Nordrhein Westfalen | Germany | 2 months ago
eager to join an international community of scientists to tackle novel and exciting research projects in the areas of biochemistry, structural biology, biophysics, cell biology, chemical biology and
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to climate change. It builds up a national and international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate change
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complex, involving multiple senders and receivers interacting simultaneously within a dynamic network. Social groups also exhibit preferred and avoided associations, creating heterogeneous social structures
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to adapt to climate change. It builds up a national and international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate
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are using ferroelectric memories, which can calculate AI algorithms from the field of deep learning in resistive crossbar structures with extremely low power consumption and high speed. Furthermore, we
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PhD position - Stress-testing future climate-resilient city and neighbourhood concepts (Test4Stress)
builds up a national and international network structure in order to integrate existing competences and knowledge, and to link various actors within the complex area of climate change. The PhD position is
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, structural biology, biophysics, cell biology, chemical biology and synthetic biology? The International Max Planck Research School for Living Matter: from molecules to dynamics (IMPRS-LM) is a joint PhD
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to the computational complexity of climate models, these will be replaced by physics-informed deep learning surrogates in the aforementioned model coupling. The project will initially focus on one main application
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for improved understanding of structural and kinetic processes in electrolytes; and machine learning concepts for improved analysis of experimental and simulated data. Material Synthesis Within this research