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models and Bayesian approaches to tackle complex, real-world data? Join this PhD project to build dynamic models and study cognitive variability using ecological momentary assessment (EMA). Join us We are
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PhD MSCA - Acoustic and Ultrasound-based Predictive Maintenance Systems for Industrial Equipment Power converters are essential in numerous applications such as industry, photovoltaic systems
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-vestibular), input-related, and voluntary movement-related determinants of HD signals in the mouse model, to elucidate their role in goal-directed navigation. The candidate will study thalamo-cortical and
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modelling. Bayesian fusion/inference methods will also be integrated for state estimation, uncertainty quantification, anomaly detection, remaining-life prediction and operational optimisation. Research aims
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Institut de Recherche pour le Développement (IRD) | Montpellier, Languedoc Roussillon | France | 1 day ago
these datasets, build models of carbon stock dynamics in ecosystems. Your work will contribute specifically to objective (2), focusing on quantitative IR spectroscopy. Over the past thirty years, many research
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warning and adaptive control to ensure safe and reliable human–robot interaction. The PhD project will develop AI-empowered predictive models that anticipate network delay and instability using historical
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: how can physics-based flow modelling be coupled with advanced optical sensing to infer methane emission rates accurately and with quantified uncertainty? The project will pursue three core objectives
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-enabled infrastructures. ToM-Enabled Multi-Agent Cooperation: Equip autonomous agents to model the mental states of other agents, allowing prediction of behaviours, conflict resolution, and coordinated
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. Robust reservoir-scale modelling is therefore essential for predicting system performance and informing design and operational decisions. Leveraging geological, hydrological, and thermal models developed
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validate the concept. This includes: - Translation of simulation insights into reactor design - Experimental investigation of particle retention and transport - Comparison between model predictions and