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be a game changer. Deep learning models can learn the mapping between material states and ultrasonic responses from simulation data, delivering quantitative predictions once trained, and remarkably
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complex contexts through statistical models, machine learning (ML) methods, and artificial intelligence (AI). This includes working with performance, scalability, resilience regarding platform architectures
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biology approaches. • Previous work on neurobiology of marine systems or of genetic models is preferable. • Fluent (B2+) in oral English The Plankton Senses Team is looking for a highly motivated and
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of control systems theory to create a new generation of intelligent underwater robotic systems. The research will focus on developing hybrid learning–control architectures that integrate model-based control
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Engineering. DCSC is an internationally leading research centre where PhD candidates, postdocs and faculty members work on modeling, analysis, control and optimization of complex dynamical systems relevant
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and experimental models to study metabolic communication in GBM invasion. In this context, spatial transcriptomics and mass spectrometry imaging provide tissue-level maps of tumour–microenvironment
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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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requirements for training data, memory, and computing power while maintaining a high level of accuracy. Particular attention will be paid to the explainability and embedding of the models to facilitate
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responsible for pressure generation and the physical factors limiting eversion. Finally, the experimental data will be integrated into a biophysical model linking pressure, geometry, and tissue mechanical
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include: - Extension to radio and optical diagnostics to test different interaction scenarios - Extension to low mass binary interactions at optical wavelengths - Development of empirically motivated models