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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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: This PhD project will develop model- and data-driven hybrid machine learning material models that capture the complex, nonlinear, path- and history-dependent behaviour of materials. The goal is to create
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(i.e. relationally interdependent systems) and encoding nonlinearities in these. The group has plentiful in-house simulation capabilities of numerical models and access to extensive real-world monitoring
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to: - Developing underwater communication systems using deep learning which are well-performing to nonlinear channels. - Establishing a deep learning architecture which is optimal for underwater acoustic
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scientific queries to Dr. Christian Bayer (christian.bayer(at)wias-berlin.de ) and Prof. Dr. Peter Bank (bank(at)math.tu-berlin.de ). The position is available immediately, remunerated at WIAS (50%) in