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. Skills in numerical tools and programming are desirable (MATLAB, python, C++ etc). Any experiences with engineering design, structural/aerodynamic/aeroelasticity modelling, manufacturing/assembly process
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steel technology enabled by advanced manufacturing processes and emerging magnetic materials for applications across automotive, aerospace, and power generation. Starting from modelling and parametric
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This PhD forms part of an ongoing collaboration between IBM and the Department of Engineering at the University of Cambridge, centred on the mathematical and computational modelling of Earth and
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datasets (e.g. ground-based radar measurements and weather station data) Contribute to the development and application of numerical models Assess uncertainties for future sea-level projections Publish
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an appropriate set of dimensionless numbers (including Péclet number, Damköhler number, capillary number and viscosity ratio). The project focuses on formulating this coupled model and implementing it numerically
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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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The Doctoral Candidate will: Perform numerical modelling of the three NDE techniques to evaluate the influence of relevant material property gradients on each NDE observable generating a sizable synthetic
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induced seismicity. Current models remain limited by the scarcity, heterogeneity, and noise of available data, as well as by incomplete knowledge of the subsurface. Physics-Informed Neural Networks (PINNs
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Inria, the French national research institute for the digital sciences | Villers les Nancy, Lorraine | France | 26 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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numerical approach The PhD is part of a Franco-German co-funded project between IFP Energies Nouvelles (IFPEN) at Lyon and the Hamburg University of Technology (TUHH), focusing on the modelling of gas/liquid