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capable of automatically selecting the simulation strategy best suited to a given prediction objective, while optimizing the trade-off between accuracy and computational cost. The work will build on multi
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-turbine capable of converting their energy into electricity. Ultimately, this strategy could significantly reduce the net energy consumption of the process and contribute to the development of low-carbon
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subsets in cancer immunity and immunotherapy. We will develop a research topic focusing on innovative cancer vaccine (T helper vaccine, mRNA…), optimized CAR-T approaches (introduction of a new companion
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young researchers in connection with the École Doctorale du Pacifique, disseminate results to society, and optimize resources by pooling equipment and means. Thus, the PhD student will have privileged
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finite element methods (FEM) and deep learning approaches. Differentiable FEM enables direct access to sensitivity information within non-linear and coupled systems, while neural networks can be used
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neural network tool to design multilayered compound metasurfaces for multifunctional operation. • Collaborate with nanofabrication teams to prototype and validate designs. • Conduct optical