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network capable of both inference and learning, aiming at investigating candidate architectures for ASIC design in a longer-term future beyond the scope of this PhD work. Thesis objectives As part of
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. Experimental characterization of Hall effect thrusters using combination of diagnostic techniques such as optical emission and absorption, Langmuir probes, etc. enhanced by the application of machine learning
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SICAL team at LIRIS, recognised for its expertise in HCI and education, including adaptive gamification, engagement, learning analysis, and the design of motivational affordances in education. They will
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Offer Description Funding: 36 months, CIFRE (https://www.anrt.asso.fr/fr/le-dispositif-cifre-7844 ) Starting date: November / December 2025 Keywords: Physically informed machine learning, Industrial
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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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hydrogen (H₂), aiming to develop efficient, carbon-free fuel blends for sustainable energy systems. Detailed simulations of these new systems rely on flame properties such as the laminar burning velocity and
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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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for automatic process generation. Generative approaches, using deep learning algorithms, can generate new process structures, surpassing conventional optimization techniques. Objectives of the ATHENA project
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of the team but speaking French or an interest to learn French will be advantageous. Persons from historically underrepresented groups in academia are especially encouraged to apply. We are hiring a
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: electronic structure calculations (plane wave DFT if possible), statistical thermodynamics, molecular dynamics. Skills in Python, bash scripting, Fortran 90 and machine-learning would be appreciated. The PIIM