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, but also to develop approaches to prevent damage. In the project we aim to develop a numerical model to predict root growth. Your task as a Postdoc researcher is to develop a measurement and monitoring
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: Computational materials modeling: DFT, molecular dynamics, phase-field modeling, or multiscale simulations. Data-driven materials discovery: ML models for property prediction, materials design, or synthesis
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. Erben at the Ludwig Boltzmann Institute of Osteology in Vienna, Austria. The project employs a novel stress-shielding model in sheep in combination with multi-omics and systems biology to dissect the role
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at the Ludwig Boltzmann Institute of Osteology in Vienna, Austria. The project employs a combination of in vivo studies using different transgenic mouse models with in vitro cell and tissue culture approaches
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field observations by including the local impact conditions on the blade with the cumulative damage analysis. Moreover, a predictive tool will be developed that allows site-specific analysis based
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interest in social science applications, and with strong competence in statistics and machine learning. The successful candidate will develop predictive models using machine learning and work alongside other
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(that might predict learning and outcomes), underlying cognitive and neurobiological mechanisms, and intervention outcomes using tools including, but not limited to brain imaging (broadly defined
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operando data related to battery degradation and safety. You will develop and implement advanced deep learning models to analyse multi-modal operando data from accelerated stress testing, with the aim
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including applications to biology , condensed matter analogues of axions , Condensed matter theory , Cosmology , General relativity , Phenomenology , Physics Beyond the Standard Model , Physics of cold atoms
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clear, auditable explanations of assessments and predictions, ensuring analysts and operators understand model outputs in time-sensitive security scenarios. Real-time intelligence fusion and rapid