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Field
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modelling, data assimilation, and multi-scale neural network architectures applied to spatio-temporal data. The development of these methods is motivated by a concrete and important application: inferring gas
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theoretical and computational methods to describe electronic, optical, and transport properties of complex materials. The first advertised project (a) is dedicated to the methodological enhancement
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you often find yourself fascinated by the multiscale complexity of real-world engineering problems? In this project you will develop advanced multiphase transport models for porous systems, bridging
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scope of the PhD position(s) fall within the areas of novel low-complexity neural network architectures, generative audio techniques, and the integration of large language and speech foundation models
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machine learning models to extend their compatibility with problems from mechanical engineering and materials research that could not be addressed so far due to their high complexity, which prevents
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pollution, support biodiversity and deliver social value. Yet these trade-offs remain poorly quantified in complex urban landscapes. This PhD will investigate how urban blue networks can be optimised for both
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operation in hybrid quantum–classical networks across intra- and inter-domain settings. The candidate will study architectural models, including the placement and roles of quantum repeaters, memories, and
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Research Infrastructure? No Offer Description Chromatographic problem solving, commonly referred to as method development, is hugely complex, given the many chemical parameters that need to be optimized and
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About Us The successful candidate will join our new MSCA Doctoral Network: VISIONBRAIN - Cutting-edge Human In Vitro And In Silico Biomedical Tools On Brain Disorders. This EU-funded project will
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knowledge co-evolution and addressing complex challenges in a super-intelligent society. This project is situated within the rapidly evolving field of Cyber-Physical-Social Systems (CPSS), which is of