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. The team is currently composed of 6 permanent members, 4 PhD. student, 3 Post Doc and 1 master's degree. All the skills necessary for the successful of the thesis are mastered by all members of the team. Web
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laboratory team is likewise highly recognized for its research in computer vision and neuro-inspired artificial learning. Both teams have been collaborating for four years on projects at the interface between
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on food craving and health-related decision-making. To this purpose, we will use a combination of brain imaging, behavioral measures, and machine-learning techniques. Activities The successful candidate
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. The project proposes an innovative approach to model sea ice dynamics from the ice floe scale to the basin scale, leveraging hybrid data assimilation and machine learning methods to shape a physically robust
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Microelectronics teams, the PhD student will be supervised and helped. He/She will access, after training, the IEMN technological platforms. He/She will be provided the tools and computer accesses necessary
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parameters to identify regimes that ensure both flame stability and low pollutant emissions. Machine learning techniques have recently shown promise for Design of Experiments (DoE) and interpretation of large
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into **influence functions**, theoretical tools designed to quantify the impact of a sample on a machine learning model. These functions, defined through the derivative of model parameters or the loss function with
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published openly in the form of process flowsheet databases. Skills: Machine Learning/Deep Learning skills are essential, as well as programming proficiency, as well as some knowledge of energy or process
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, engineering, bioinformatics, machine learning, artificial intelligence) to support minimally invasive and targeted preventive and predictive medicine capable of limiting age-related functional disorders
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by exploiting foundational machine-learning potentials such as MACE, SevenNet, or Orb-V3. The predictions will then be progressively refined and verified by DFT and, ultimately, tested experimentally