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to correlate polymerisation kinetics, macromolecular architecture, morphological evolution and drug encapsulation mechanisms. Beyond experimental work, the project will integrate machine learning approaches
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Discrete Element Method (DEM) software GranOO. Expected Outcomes The digital twin approach will be validated against experimental results through the comparison of: (i) thermomechanical properties such as
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: establishment of relationships between microstructure and macroscopic thermomechanical properties of industrial refractory materials, collection of an experimental database combining conventional characterization
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Perception and Action research team under the supervision of Dr Dollyane Muret. Experimental work will be carried out at the INCC BabyLab (https://babylab.incc-paris.fr/ ), a research facility dedicated to
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. The goal is to identify the factors that enable stable and homogeneous growth, leading to an extremely low density of structural defects. A comprehensive approach, combining high-temperature experimentation
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, biophysics, biochemistry, and molecular modeling, developing experimental and numerical approaches for the study of complex molecular systems. Within this institute, the Biophysics of Complex Systems team
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to the development of energy models; • to access complementary experimental platforms; • to strengthen Franco-Canadian scientific collaborations. The project also benefits from industrial support through
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the experimental protocols and developing metrology methods to quantify improvements in imaging depth, preservation of tissue architecture, and overall imaging performance compared with conventional serial
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interdisciplinary environment combining chemistry, biology, and physics, with access to shared experimental infrastructures. They will be affiliated with the PHENIICS Doctoral School (link) under the “Physics and
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with a background in fluid mechanics or thermal engineering. Given the scope of the research project, a strong interest in both experimental work and numerical modeling is essential. Familiarity with