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, machine learning techniques, etc.) is desirable. This thesis offer within the AstroParticle and Cosmology Laboratory (APC) is part of the Deep Underground Neutrino Experiment (DUNE). DUNE is an
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of this brain cancer, as well as the evolution of the tumor. This part of the project involves designing, developping, and running machine learning algorithms and approaches to exploit vectors of RNA modification
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the mobility of IoT devices. This thesis proposes leveraging intelligent softwarization—using Machine Learning (ML), Software-Defined Networking (SDN), and Network Function Virtualization (NFV
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new insights into the phenomena observed and enrich the databases required for deep learning methods. The neural networks currently being developed at LISTIC to detect and segment areas of movement in
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on the development of advanced artificial intelligence and machine learning methods for genome interpretation, with a particular emphasis on modeling the relationship between genetic variation and phenotypic outcomes
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for experimentation, yet they remain difficult to deploy directly onboard robots due to hardware availability, latency, sampling cost, and noise. Previous work on quantum machine learning (QML) emphasize
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automated configuration mechanisms based on fingerprinting and machine learning to ensure traffic analysis remains faithful to the behavior of the monitored machines. Finally, you will validate your solutions
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minimizing computational and energy costs. The proposed approaches will rely on machine learning methods applied to image analysis, with the objective of enabling early identification of at risk areas and
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models Foundation models represent a breakthrough in AI, as did the shift from traditional machine learning to deep learning. Numerous models become available in the field of Earth Observation and can be
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to reduce the cost of clean hydrogen to $1/kg by 2031. The project proposes to address key scientific challenges by using molecular simulations (reactive force fields like ReaxFF and machine learning