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the properties of composite, biological networks which consist of stiff filaments and liquid inclusions which arise from liquid-liquid phase separation. We will use a multi-scale approach bridging scales from
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organizational levels of the brain – from molecular and cellular processes to complex neuronal networks and behavior. In association with the SFB 1436, Neural Resources of Cognition (supported by the German
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in an international team in an EU-funded Doctoral Network project called MINDnet. The project consists of 15 PhD students at 7 universities, one research center and two companies. The project has
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surrogates or approximators, such as random forests or shallow neural networks, trained to mimic the outputs of the original computations at a fraction of the cost. This hybridization aims not only
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in complexity to a network-shaped single cell – Physarum. Lacking any neurons, flows flushing throughout Physarum’s tubular network propagate input packaged as chemical concentration and flow shear
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industrial engineering of similar Interest in the practical testing of catalysts in laboratory-scale pilot plants Enjoyment of exploratory work with great scientific potential Ability to analyse complex
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instances to solve new, yet similar, instances more efficiently than with general purpose algorithms such as Netwon`s method. In particular, we aim to develop a neural network architecture that will allow us
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geometries. Current simulation-based approaches require complex 3D meshes and are often too slow for practical medical use. This project aims to create accurate and rapid surrogate models by combining physics
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simulation of technical systems Interest in practical work with catalysts, laboratory setups and pilot-scale plants Ability to analyse complex interrelationships and work methodically Good written and spoken
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learning, physics-informed neural networks, graph neural networks, transformers, convolutional defiltering methods, etc.) for the integration in multi-physics simulation codes You will develop code for and