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for an Active Ankle Foot Orthosis", Control Engineering Practice, vol. 169, 2026, 106757, doi: https://doi.org/10.1016/j.conengprac.2026.106757 . [4] O. Bey, Y. Amirat, S. Mohammed, "Adaptive Model-Free Control
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, vol. 167, p. 115644, Mar. 2025. https://doi.org/10.1016/j.microrel.2025.115644 [2] A. Bender, “A Multi-Model-Particle Filtering-Based Prognostic Approach to Consider Uncertainties in RUL Predictions
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systems capable of selecting and combining models of different complexity, in order to better represent groundwater dynamics and improve large-scale predictions under climate change. Objective — The PhD
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Inria, the French national research institute for the digital sciences | Montbonnot Saint Martin, Rhone Alpes | France | 11 days ago
the prediction of vehicle flows, energy demand, and flexibility of electric vehicle fleets, with applications to energy and transportation systems. The work lies at the intersection of systems and control, data
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to crack initiation and assess the influence of the microstructure of Almelec alloys. The results will also be used to improve a predictive FEM model (Abaqus/Python) simulating crack initiation and
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to identify and model the most efficient catalytic sites on NDs using advanced Density Functional Theory (DFT) calculations. The project seeks to revolutionize the design of ND-based catalysts by controlling
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an integrated framework for co-design and adaptive operation of electrical networks. DynConGrid develops real-time Model Predictive Control (MPC) for congestion management by reconfiguring topology, curtailing
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settings. The ultimate goal is to enable early, systematic, and robust screening of children at risk of neurodevelopmental disorders. Deep learning models typically produce point predictions, whose
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control and prediction of degradation kinetics in physiological environments remain major scientific challenges. This PhD project aims to establish fundamental and quantitative understanding of dissolution
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. The successful applicant will develop a predictive pipeline using atomistic modeling and machine learning to identify optimal "seeds" for directing crystal growth, followed by rigorous experimental testing