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contribute to the development of predictive human in vitro models for ageing research while providing robust alternatives to animal experimentation. Where to apply Website https://emploi.cnrs.fr/Offres
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in development to ensure the materials are safe, affordable, and user-friendly. The project will also explore behavioural drivers, incentives, and innovative business models to stimulate adoption
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to predict traffic demand, user mobility, and network conditions, enabling autonomous decision-making that maximizes network performance and user experience. A unique aspect of this research is the integration
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refines algorithms and workflows for crop and pasture monitoring, modeling, prediction, and decision support and automation; Supervises graduate research assistants and student interns working
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and does not fully exploit real-time satellite data for nationwide dynamic prediction. Recent advances in deep learning have improved performance in flood mapping tasks, yet these models often remain
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traffic demands increase, there is a growing need for innovative methods to continuously assess track condition and predict deterioration. This PhD project addresses this challenge by developing a novel
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, including efficiency, fairness, reliability, and sustainability. Despite recent advances, existing mathematical models often fail to jointly capture these aspects and trade-offs, limiting their ability
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are looking for a motivated and talented PhD candidate to join a unique interdisciplinary project at the intersection of machine learning and formal methods. Information Machine learning models deployed in real
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Application deadline: 30.09.26 Research theme: Applied Mathematics, Thermal-Fluid-Dynamics, Multi-Physics How to apply: https://uom.link/pgr-apply-2425 UK only This 4 year PhD project is fully
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of machine-learning-infused atomistic modeling techniques and their application to important problems in chemistry, physics and materials science. Together, you will help advance a key scientific