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PhD Candidate to conduct research on Artificial Intelligence for managing Shipbuilding Supply Chains
methodologies, such as simulation, analytical modelling, and AI‑driven techniques, to develop decision support for efficient planning and coordination of production activities in supply chains and generate
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interpret thermodynamic datasets describing element behavior under EAF and REF conditions Integrate experimental results into thermochemical models (e.g. FACTSage) to support process simulation and
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robust management of large traffic networks under all conditions. You have the most important role in this ambitious project as one of the young talents in our team. We have 2 PhD and one postdoc positions
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of the young talents in our team. We have 2 PhD and one postdoc positions, all of whom will be supervised by a highly experienced team of four (top) researchers in this field supported by a technician. You will
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optimal ways to schematize and parameterize the subsurface at local to regional scales to assess dike safety using geological and geohydrological data and models, for characteristic fluvial landscapes in
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components. You will explore how learning-based methods, such as imitation learning and reinforcement learning, can be integrated with model-based low-level controllers and multimodal sensing to enable contact
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large-scale omics datasets, develop and apply statistical methods and interpretable AI models, and contribute to the identification of biological markers and molecular mechanisms associated with disease
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of Sciences (UCAS), integrated into the MOE Social Science Laboratory of Digital Economic Forecasts and Policy Simulation led by Professor Ying Liu. There is an active group of PhD students and postdocs working