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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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be done via computer simulations, including Monte Carlo and molecular dynamics, combined with the use of statistical mechanics to predict e.g. phase transitions, nucleation rates, etc. The work will be
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for such applications. To respond to these challenges, this project aims to investigate automated decision making based on machine learning. The candidate (H/F) will propose and validate centralized as
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that are transforming many sectors today through language models, recommendation systems and advanced technologies. However, modern machine learning models, such as neural networks and ensemble models, remain largely
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of machine learning algorithms are of real interest in improving the accuracy of water quality measurements, particularly in identifying, accounting for, and neutralizing ionic interference. The second key
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teams. Nearly 90 doctoral students, a dozen post-docs, 60 master's or engineering school interns complete the workforce. Attached to the CNRS Institute of Computer Science, its research themes cover 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
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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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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