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Field
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competitive ERC. The project focuses on the development of a first-principles, machine-learning-accelerated computational framework for modelling polymorphism, anharmonicity, and electron–phonon interactions in
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interaction with the road infrastructure. • Experience in big data analysis and handling of large data sets, with skills in applying AI and machine learning techniques to traffic pattern analysis and road
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coupled hydrological and groundwater modelling, combined with machine learning techniques, will quantify groundwater recharge and groundwater resilience. Your responsibilities: Analyse the dynamics
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and patient-reported outcomes; (b) observational research and comparative effectiveness studies; (c) intervention studies; (d) clinical informatics, mobile/electronic health; (e) machine learning
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energy efficiency and intelligent systems for industrial applications. Based within the School of Engineering and Architecture, IERG develops and applies cutting-edge analytics, modelling, and decision
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English and Chinese Able to work independently and in a multidisciplinary team Experience in image analysis packages such as Freesurfer, FSL, SPM, or 3DSlicer, or using machine learning or artificial
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and digitizing archival data, strong knowledge of causal inference methods, good command of R and Python. Knowledge of machine learning methods is an asset. Strong command of English; command of either
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campaigns or model SOA formation and deposition processes. The main duty of the Postdoctoral Researcher is to conduct research related to the project and to report results in international scientific
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and machine learning. Topics of interest in this area include, but are not limited to: natural language processing, large language models, graph learning, prompt engineering, knowledge graphs, knowledge
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. This involves the development of mathematical models for signal transmission/reception, derivation of performance limits, algorithmic-level system design and performance evaluation via computer simulations and/or