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used to develop networks capable of self-learning and self-optimisation, adapting to real-time changes in traffic and demand. The successful candidate will contribute to designing solutions that optimise
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study and other ongoing translational initiatives to develop a voice-based digital health solution to alleviate the diabetes burden. Project objective The PhD candidate will work at the interface
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The Machine Learning for Integrative Genomics team at Institut Pasteur, headed by Laura Cantini, works at the interface of machine learning and biology, developing innovative machine learning
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patient clusters and digital phenotypes, leveraging machine learning approaches to identify individuals at high CV risk based on clinical and biochemical markers, immune markers, digital health data (e.g
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progression. This project addresses the challenge of studying early tumorigenesis in pancreatic cancer by developing advanced in vitro models based on human extracellular matrix (ECM), aiming to recreate
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controlled via structural phase transitions or external fields. The successful candidate will develop and apply a range of theoretical and computational methods based on first-principles electronic structure
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particular NLP, statistical learning, machine learning, generative AI, and their major fields of application. Roles and responsibilities The applicant will join the team of the 3IA Côte d’Azur Institute and
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Simulation and Physics of Drosophila Larva Body Dynamics Introduction The “STRETCHED” project aims to develop a robust, physics-based 3D simulation platform to replicate the motor control dynamics
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of Health, and Luxembourg Institute of Science and Technology. MICRO-PATH addresses research questions based on causal and mechanistic studies of microbiome-mediated pathogenesis. The vision