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nanostructures. You will fabricate your own samples in the clean room (thin-film deposition, electron-beam and photolithography, and SEM/FIB-based 3D nanostructuring) and probe their static and dynamic response
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white matter. The network aims to strengthen the full translational pathway, from understanding disease mechanisms and developing disease models to preclinical therapy testing, clinical readiness and
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. To address this challenge, the project aims to develop structural health monitoring technologies based on a digital twin. The digital twin will combine information from the physical structure with models and
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. Preferential Requirements Additional professional experience in laboratory management, as indicated in the CV and/or motivation letter; Knowledge in 3D cell models, including spheroids, cloning and CRISPR/Cas9
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to design an AI system that integrates scientific knowledge for early dementia modelling, based on large language models and causal inference. The project has two stages: first, to construct a causal
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user-informed machine learning models that incorporate geological/geotechnical priors to interpolate 3D rock mass properties between sparse data points. Architect and implement a new stope optimization
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phenotyping, microbiome profiling, predictive modelling and targeted experimental validation to identify the microbial factors that make plants more resilient to disease. Your job You will join the Plant
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and the environment. The developed methods will combine wireless sensing with digital map information (e.g. 3D building models), enabling safe autonomous navigation in complex urban environments
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-learning models that connect powder characteristics and process parameters with the properties of the final components. These models will support faster feedstock qualification and enable predictive quality
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to the development of state-of-the-art nuclear-reaction models and evaluated nuclear-data libraries, supporting safe, reliable, and competitive technologies for both existing and future nuclear-energy systems, as