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containing CO2-negative SCMs, bio-based self-healing, hybrid fiber architecture, Fe-SMA-based self-prestressing, and integrated structural health monitoring into one coherent system for thin, durable
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magnitude, spatial distribution and temporal dynamics of mechanical cues shape cellular responses. You will investigate an integrated cell–material system in which living cells are mechanically coupled to a
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and their associated microbial communities form an integrated functional entity—the plant holobiont—whose chemical interactions and collective metabolism play a decisive role in adapting
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French National Institute for Health Research (INSERM) | Paris 15, le de France | France | 2 months ago
campus, with the long-term goal of improving patient care. The teaching program associated with the INSERM Chair will focus on fundamental and integrative immunology, with a particular emphasis on
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are developing a suite of models to deliver a through-process microstructural and mechanical property prediction framework for steel. By integrating these models into a cohesive digital twin architecture, the work
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currently seeking to recruit a PhD student to work within the centre on the topic of advanced integrated lasers for biophotonics applications. The successful candidate will join the Nanophotonics group in
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smart illumination design and advanced AI-driven data processing. Miniaturization of the sensing platform will be achieved through the use of freeform optics, enabling the integration of multiple optical
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trajectories of exonic enhancers, across vertebrate species. It aims to clarify how dual coding and regulatory constraints jointly shape the evolution of exonic enhancer sequences. The PhD student will integrate
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multi-fidelity modelling, your research will advance and integrate three core elements: (i) physics-based multi-fidelity structural models enabling high-resolution analysis at fatigue-prone hot-spots
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; profile root and leaf microbiomes using amplicon sequencing; analyse integrated microbiome and phenotyping datasets; contribute to machine-learning models predicting pathogen invasion success and plant