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. Other data sources could be compiled to create climate analogues. 2) Develop a predictive model forecasting the future impacts of climate change on cardiorespiratory fitness in children and adolescents 3
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and designs involving urban form, materiality, and nature-based solutions can provide thermal comfort throughout the year in a Nordic climate? Is it possible to predict dynamic outdoor thermal comfort
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 29 days ago
to intertwine a multi-contact whole-body controller, a digital simulation of the interacting humans, and machine learning models to predict and respond to human movements and intentions. In a crescendo of
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to study and predict. In this four-year SNF-funded project, you will develop data-driven, multiscale simulation methods that combine computer simulations, machine learning, and surrogate models to explore
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sensing systems Design and validate machine learning models for predictive monitoring of physiological states Analyse large experimental datasets and quantify sensor performance (accuracy, robustness
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focuses on AI-driven fault diagnosis, predictive analytics, and embedded self-healing mechanisms, with applications in aerospace, robotics, smart energy, and industrial automation. Based
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into areas such as AI-driven verification, predictive maintenance, and compliance assurance, aiming to enhance system reliability and safety. Situated within the esteemed IVHM Centre and supported by
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fundamental AI methods together with their software implementations for interpretable statistical fault prediction and lifetime assessment in the context of Structural Health Monitoring of operating wind
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crystallization from mixed salt solutions and elucidate salt weathering mechanisms at micro and macro scales in heritage materials. Your work will bridge the gap between theoretical predictions and real-world
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plus. Previous experience and strong interest in control and data-driven modelling. For instance, a previous experience with model predictive control, reinforcement learning, or automatic demand response