Sort by
Refine Your Search
-
Listed
-
Category
-
Program
-
Employer
- CNRS
- Inria, the French national research institute for the digital sciences
- INSERM
- UNIVERSITE DE TECHNOLOGIE DE COMPIEGNE
- Universite de Montpellier
- 3IA Côte d'Azur
- Aix-Marseille Université
- BRGM
- Grenoble INP - Institute of Engineering
- IMT Atlantique
- INSA Strasbourg
- Institut Agro Rennes-Angers
- Institut Pasteur
- LEAD CNRS UMR5022, Université de Bourgogne
- Luxembourg Institute of Science and Technology (LIST)
- Mines Paris-PSL
- Université Marie et Louis Pasteur
- Université d'Orléans
- Université de Bordeaux / University of Bordeaux
- Université de Caen Normandie
- Université de Picardie - Jules Verne
- Université de Toulouse
- 12 more »
- « less
-
Field
-
have access to state-of-the-art facilities, including cleanroom microfabrication, laser micromachining, 3D printing, advanced microscopy, and numerical modeling tools. The project benefits from a close
-
into advanced manufacturing processes. The overall objective is to develop a reliable and predictive digital twin dedicated to polymer processing technologies, particularly injection moulding, by combining
-
interferometry, etc.), (v) Predictive modelling of coupled phenomena (reactive transport, rock-water interactions, etc.), (vi) Uncertainty quantification (Monte Carlo, meta-modelling), and (vii) Risk analysis. The
-
predictive digital twin of the face to better understand, model, and rehabilitate facial expressions. The project is led by the BMBI laboratory (UTC-CNRS) and brings together a multidisciplinary consortium
-
of these sensors. - Access to the laboratory's experimental characterization facilities. - A collaborative and multidisciplinary environment at the interface of theory, modeling, and experimentation. Scientific
-
data) will help validate observations and refine predictive models. Automated monitoring tools (scripts, dashboards, alerts) incorporating machine learning algorithms or statistical methods will be
-
of materials, the extraction process, etc.), can predict the characteristics of the bioplastic (transparency, impermeability, strength, etc.). The second model will be the “inverse” model; it will use
-
rules for ceramic shell mould clusters; Experimental validation of the relationships between microstructure and thermal shock resistance; Development of multi-scale finite element models for predicting
-
significantly to the dose delivered to biological tissues. Monte Carlo simulations, especially those based on the Geant4 toolkit, rely on hadronic models whose predictive accuracy is still limited by
-
to contextualise future observations of Jupiter without an external monitor of solar wind conditions (e.g. the JUICE probe), (iii) making more accurate predictions for exoplanetary radio emissions to aid in