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mangament in numerical models, including advanced calibration strategies from data (observations, measurements, other model predictions) and uncertainty reduction. Scientific context Many engineering and
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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validated algorithms. The objective is to identify sensorimotor signatures of fall risk that may improve current predictive models and contribute to the development of more targeted prevention strategies
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macro- scales at IJL, and to train machine learning models to predict the microstructure evolution at larger scales and longer times at SIMAP lab and Laboratoire Analyse et Modélisation pour la Biologie
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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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Institut National des Sciences Appliquées de Lyon | Villeurbanne, Rhone Alpes | France | 12 days ago
solutions for vibration and noise control in lightweight structures (https://cordis.europa.eu/project/id/101227712 ). The project focuses on the development of Acoustic Black Hole (ABH) technologies
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modeling, photovoltaics, high-temperature experimentation, and solar energy technologies. Thermophotovoltaic (TPV) systems convert thermal radiation emitted by a hot surface into electricity using lowbandgap
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prediction models, and visualizing immense volumes of various types of data, generated by agri-robots and IoT devices. The most popular classes of autonomous agricultural devices include: weeding robots
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for control” funded by the EU Partnership on Animal Health and Welfare (EUPAHW, https://www.eupahw.eu/ ). Supervisors Dr Timothée Vergne is an Associate Professor of Veterinary Public Health at the National
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analysis) to compare brain responses with predictions of computational models (deep neural networks developed by the NASCE team). The objectives include assessing how the brain segments, groups